Machine learning final year project ideas
Machine learning final year project ideas from the free Sefism library, each scoped for a single semester with a suggested stack and deliverables.
- 300 project ideas
Ideas and guidance, not finished projects
These are project ideas and scoping guidance, published free for students to use as a starting point. I do not build, write, or sell final-year projects, and I do not complete coursework for anyone. Take an idea, make it yours, and build it.
101 ideas · page 1 of 5
Multi-Agent Literature Review Assistant with Verified Citations for Students
Students writing a literature review often get chatbot answers full of papers that do not exist. Build a multi-agent assistant that plans a search, queries open scholarly databases, reads abstracts and open-access full texts, drafts a structured review, and refuses to cite anything it cannot resolve to a real DOI or arXiv record with a supporting passage. Then measure whether multiple agents actually beat a single agent on quality and cost.
Next 48-Hour PM2.5 Forecasts for South Asian Cities from Open Sensors and Weather Data
Each winter, smog in Lahore, Delhi, Dhaka and Kathmandu closes schools and harms health, and decisions are made with little warning. Build a model that forecasts PM2.5 for the next 48 hours at city sensor locations, using open sensor readings, weather forecasts and fire detections, and compare it against the global CAMS forecast. Present it as a simple outlook a school or clinic can use to plan.
Antenatal Follow-Up Prioritiser: Which Pregnant Women a Health Worker Should Call This Week
A health worker with 150 pregnant women on her list cannot call all of them every week. Build a mobile app and ranking model that suggests who to contact first, based on missed visits, risk factors and engagement history, and measures whether the ranking finds women who would otherwise drop out of antenatal care. The project is about fair, explainable prioritisation under a fixed call budget, not about diagnosing anyone.
TinyML Sound and Vibration Anomaly Detection for Backup Generators
Shops, clinics, mosques and offices rely on diesel or petrol backup generators during power cuts, and they fail at the worst moment. Build a small sensor node that listens to a generator's sound and vibration, learns its normal behaviour on the device itself with a tiny anomaly detection model, and sends an alert when something sounds wrong, without a cloud connection or a data scientist.
Which LLM Understands Roman Urdu and Bangla Business Messages? A Three-Model Evaluation Harness
Small businesses in Pakistan and Bangladesh get orders and complaints as WhatsApp-style messages in Roman Urdu, Urdu script, Bangla and English mixed together. Build an evaluation harness that compares three LLMs (for example one hosted frontier model, one cheaper hosted model and one open model) on real-shaped tasks such as extracting order details and classifying complaints, with a human-checked gold set, cost per correct answer and a public results page.
Model Card and Datasheet Generator from Training Runs
Model cards and dataset datasheets are the standard way to document what a model does, what data it learned from and where it fails, but they are written by hand at the end, if at all. Build a generator that pulls facts from MLflow runs, dataset files and evaluation results, drafts a model card and datasheet, flags missing or unsupported sections, and keeps the card in sync as the model is retrained.
Mobile Money Agent Cash and E-Float Forecasting to Prevent Turned-Away Customers
Mobile money agents in Kenya, Pakistan, Bangladesh and Nigeria lose customers when they run out of cash or electronic float, especially on salary days and before holidays. Build forecasting models for each agent's cash and e-float needs from simulated transaction histories with realistic calendars, and a simple app that tells the agent and the aggregator when and how much to rebalance. Measure how many turned-away transactions the forecasts would prevent.
Offline AI Tutor Hotspot on a Raspberry Pi for Schools Without Internet
Many schools have a few phones or tablets but no reliable internet. Build a Raspberry Pi box that runs a small open language model and a curriculum-grounded retrieval system completely offline, creates its own Wi-Fi network, and lets students ask questions about their textbook in Urdu or English from any browser. Teachers control what content is loaded and see which topics students ask about most.
PII and Secret Leak Audit for LLM Chat Logs and Traces with Retention Rules
LLM apps log everything: prompts, retrieved chunks, tool results and answers. Those logs quietly fill up with phone numbers, CNIC numbers, addresses, API keys and other users' data. Build an auditor that scans existing LLM traces, finds personal data and secrets in English, Urdu and Roman Urdu, shows where each leak entered the pipeline, and enforces retention and masking rules going forward.
Glacial Lake Growth Monitoring from Sentinel-2 Imagery for Outburst Flood Risk in Northern Pakistan
Glacial lakes in Gilgit-Baltistan and Chitral are growing as glaciers melt, and some can burst and flood villages downstream. Build a pipeline that maps lake outlines from free Sentinel-2 satellite images every month, tracks area change over several years, flags lakes growing fast, and lists the settlements downstream. Validate the outlines against published inventories and hand digitising.
Tool Selection Evals and Description Linter for MCP Servers
An MCP server can work perfectly and still fail in practice because models pick the wrong tool or fill its arguments badly. Build a tool that lints MCP tool names, descriptions and schemas against good-practice rules, generates realistic user requests for each tool, measures how often several models choose the right tool with valid arguments, and suggests rewrites that improve the score.
MCP Server for Public Transport Timetables Built on GTFS Feeds
Build a Model Context Protocol server that lets any AI assistant answer real transport questions correctly: next departures, journeys with transfers, service changes and accessibility, all computed from GTFS timetable data rather than guessed by the model. Where a city has no public feed, the team builds one for a route they know, such as a university shuttle or a metro bus line, and publishes it openly.
Mandi Arrival and Price Forecasting with Sell-or-Store Advice for Farmers
Farmers often sell right after harvest when wholesale market (mandi) prices are lowest. Build forecasts of arrivals and prices for a few crops at nearby markets from public government data, and turn them into simple advice: sell now, or store for a few weeks, after storage costs and losses. Test the advice by replaying past seasons and measuring how often it would have helped.
Arabic Diacritics Reading Tutor with Automatic Tashkeel Feedback for Learners
Learners of Arabic, from schoolchildren in Pakistan and Nigeria to expatriate workers in the Gulf, struggle to read text without short vowel marks (tashkeel). Build a tutor that removes the marks from a passage, asks the learner to add them, and checks the answer against an automatic diacritiser and a verified reference, explaining errors by grammar rule. Measure diacritiser accuracy and whether learners improve.
Roman Urdu Jailbreak and Policy Drift Red Team for Customer Service Chatbots
Customer service chatbots are tested in English, then deployed to users who write in Urdu, Roman Urdu and a mix of all three. Build a red-team harness that checks whether a bot stays inside its own written rules (no promises it cannot keep, no advice outside its remit, no leaking its instructions) when attacked in local languages and code-switched text, and produces a scorecard the business can act on.
Crop Disease Photo Triage That Knows When to Ask an Agronomist, on Budget Android Phones
Most crop disease apps give a confident answer for every photo, even blurry ones, the wrong crop or a disease they never saw in training. Build an on-device classifier for a few crops that abstains when unsure and sends those photos to an agronomist review queue; the agronomist's answers become new training labels. The project measures how much accuracy improves when the app is allowed to say "I don't know".
Inter-Annotator Agreement and Adjudication Tool for Data Labelling Teams
AI depends on labelled data, and labelling teams in Pakistan, India, Kenya and elsewhere produce a lot of it. Build a quality layer for labelling projects that measures agreement between annotators, spots unreliable labels and confusing guidelines, routes disagreements to an adjudicator, and estimates true labels from noisy votes, working with exports from Label Studio and similar tools.
Brand-Consistent AI Video Ad Generator with Automatic Checks and Disclosure Labels
Small brands want short video ads for Instagram, TikTok and YouTube but cannot afford a studio, and AI video tools produce clips that drift off-brand and are not clearly labelled as AI-made. Build a generator that turns a product brief and brand kit into short ads in several formats, then automatically checks every frame for logo, colours, text spelling and banned claims, and adds a visible AI label plus Content Credentials before export.
Human-Approved AI Agent for Filing and Tracking Citizen Service Requests
Filing a complaint or service request with a city or government office means finding the right portal, filling long forms, attaching documents and then chasing it for weeks. Build an agent that turns a citizen's plain description (in Urdu, Roman Urdu or English) into a correctly filled request, pauses for the citizen to approve every submission, then tracks status and reminds them to follow up. Built and tested against a realistic mock portal you create.
Drone Plant Stand Counting and Gap Mapping for Smallholder Maize and Sugarcane Fields
A few weeks after sowing, farmers need to know how many plants came up and where the gaps are, so they can resow in time. Build a pipeline that takes low-altitude RGB drone photos of a small field, stitches or tiles them, counts plants with a detection or density model, and produces a gap map and an estimate of plant population per acre. Compare it against hand counts in marked sample plots.
River Flood Forecasting with LSTM Models from Upstream Gauges and Satellite Rainfall
Train and evaluate machine learning models that forecast river discharge one to five days ahead for a few South Asian or African river basins, using upstream gauge readings, satellite rainfall and public hydrology datasets. Compare against simple persistence and published global forecasts, and report the lead time at which a warning threshold would have been crossed in past floods. This is the forecasting model, not a sensor network or an alert app.
Protocol-Locked Danger Sign Triage Assistant for Community Health Workers in Urdu, Bangla and Arabic
Build a phone assistant that walks a community health worker through a sick child visit in Urdu, Bangla or Arabic, where the triage decision comes from an encoded clinical protocol, never from a language model. The model only does what it is good at: understanding a spoken or typed answer in a local language and turning it into a protocol field. The project tests whether that split keeps the decisions safe while making the tool easier to use than a long paper form.
Acoustic Early Detection of Red Palm Weevil Larvae in Date Palm Trunks
The red palm weevil kills date palms from inside, and by the time a tree shows symptoms it is often too late. Build a low-cost clip-on sensor that records vibrations in the trunk, an embedded classifier that separates larval feeding sounds from wind, traffic and birds, and a dashboard for farm managers. Test it on recordings from infested and healthy trees with an entomology partner.
Hidden Instruction Scanner for Documents Before They Enter a RAG Knowledge Base
Build an ingestion-time scanner that inspects PDFs, Word files, web pages and spreadsheets before they are chunked into a RAG knowledge base, and finds text a human cannot see but a model will read: white or tiny text, invisible Unicode, instructions hidden in metadata, alt text and comments. Suspicious files are quarantined with a highlighted preview so a person decides. It closes the door that indirect prompt injection walks through.
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