AI & Machine Learning Final Year Projects
AI and Machine Learning projects are the highest-scoring final year projects in 2026. We have 80+ ready-made AI/ML projects with trained models, datasets, and complete source code.
AI-Powered Brain Tumor Detection System with Multi-Model Deep Learning Analysis for Medical Diagnosis
Advanced deep learning web application with 84% accuracy using VGG16, ResNet50, and MobileNetV2 models for instant brain tumor detection from MRI scans with comprehensive visualizations and confidence scoring.
₹7999.00
₹1999
AI Content Detector — Django + Machine Learning Final Year Project with Source Code
Paste any essay and find out if a human or an AI wrote it. Django + scikit-learn, sentence-level highlighting, PDF reports, REST API. Full source code included.
₹599.00
₹1999
AquaGuard — AI Water Quality Prediction Final Year Project (Flask + ML)
A Flask and scikit-learn final year project that predicts drinking water potability from 9 physicochemical parameters with WHO safety flags and risk scoring.
₹499.00
₹1999
AI Carbon Footprint Calculator - Predict Your CO2 Emissions Using Machine Learning
A machine learning-powered carbon footprint calculator built with Python and Streamlit that predicts your annual CO2 emissions based on lifestyle inputs.
₹499.00
₹1999
PomegradeAI — AI-Powered Pomegranate Quality Grading System | Final Year Project with Source Code
PomegradeAI is an end-to-end AI final year project that uses a fine-tuned EfficientNetB0 deep learning model to classify pomegranates into three quality grades through a Flutter mobile app and Django REST API backend. It achieves 97.4% test accuracy.
₹499.00
₹1999
AI-Powered Plant Disease Detection System Using CNN and Flask — Final Year Project with Source Code
A deep learning-based web application that detects plant leaf diseases from images using a PyTorch CNN model. Covers 39 disease classes across 13 crops with real-time confidence scoring, treatment guidance, weather-based risk analysis, and a Groq-powered
₹599.00
₹1999
VishGuard AI — AI-Powered Voice Phishing Detection System
VishGuard AI detects fake or manipulated voice in vishing attacks using TensorFlow, Flask, and librosa. It uses CNN/CNN+LSTM on log-mel spectrograms and delivers real-time predictions via a web interface.
₹499.00
₹1999
AirPulse - AI Air Quality Prediction System with Live Dashboard and Next-Day AQI Forecasting for Indian Cities
AirPulse is a Python Flask web application that predicts next-day Air Quality Index for 20 Indian cities using an XGBoost model trained on 6 years of CPCB pollution data, with a real-time glassmorphism dashboard powered by the WAQI API.
₹499.00
₹1999
BreastGuard AI Breast Ultrasound Image Classification with OpenCV & SVM (Benign / Malignant / Normal)
Computer vision project that classifies breast ultrasound images into three categories including Normal. OpenCV converts each scan to a 16,384-feature vector, and a GridSearchCV-tuned SVM predicts the class at 97% accuracy.
₹499.00
₹1999
AI-Powered Pest Detection & Pesticide Recommendation System - Final Year Project with Source Code
Complete AI-based pest detection system using deep learning for automated pest identification and pesticide recommendations. Perfect final year project with full source code, documentation, and deployment guide for CSE/IT students.
₹599.00
₹1999
AI-Powered Fake Review Detection System - Advanced Machine Learning Final Year Project with 99.6% Accuracy
Industry-ready Fake Review Detection System using ensemble ML (Random Forest, XGBoost, Logistic Regression) achieving 99.6% accuracy, integrated with a Flask web interface, REST API, and fully documented Python source code.
₹499.00
₹1999
Face Recognition Attendance System with Django & OpenCV - AI-Powered Final Year Project with Source Code
Real-time face recognition attendance system using Django and OpenCV — auto-marks student check-in/check-out via webcam with 128-dimensional face encoding matching, department-wise reporting, and 99%+ accuracy on standard benchmarks.
₹699.00
₹1999
AI-Powered YouTube Comment Sentiment Analyzer with Real-Time NLP & Deep Learning Models
Advanced sentiment analysis web application that analyzes YouTube comments using Machine Learning (Logistic Regression) and Deep Learning (LSTM) with 76% accuracy.
₹499.00
₹1999
AI-Powered Image Forgery Detection System with Deep Learning & Error Level Analysis
Advanced deep learning-based image forensics system combining ResNet CNN, Error Level Analysis (ELA), and ANN classifiers to detect digital image manipulations with 96%+ accuracy - perfect for final year projects in computer vision and AI.
₹599.00
₹1999
AI-Powered Vehicle Speed Detection & License Plate Recognition System - Final Year Python Project
Advanced AI-driven vehicle speed detection system with license plate recognition using YOLOv8 and OpenCV. Perfect final year college project featuring computer vision, deep learning, and web development with Django framework.
₹499.00
₹1999
AI-Powered UPI Fraud Shield: Real-Time Detection System for Secure Transactions
An innovative UPI fraud detection system using advanced machine learning to analyze transactions in real-time, preventing fraud with 99%+ accuracy. Ideal as a final year college project or best Python project for students seeking unique projects
₹6500.00
₹1999
Face Recognition and Attendance Project
A real-time attendance system that uses facial recognition to detect faces via a webcam and records attendance automatically in an Excel sheet.
₹399.00
₹1999
Frequently Asked Questions
Popular choices include face recognition systems, sentiment analysis, chatbots, recommendation engines, fraud detection, and medical image classification. These score well because they demonstrate practical AI application.
Most projects are designed to run on CPU for development. Pre-trained models are included so you don't need to retrain. For training-heavy projects, we provide Google Colab notebooks.
Our projects use TensorFlow, PyTorch, scikit-learn, Keras, OpenCV, NLTK, spaCy, and Hugging Face Transformers depending on the use case.
Yes. Every AI/ML project comes with the training dataset, pre-trained model weights, and instructions for retraining if needed.