Top 50 Research paper topics for MTech Data science Project

For an MTech in Data Science in 2026, research projects have shifted from "building a model" to optimizing systems for reliability, privacy, and specialized domains. The focus is now on Agentic AI, Small Language Models (SLMs), and Explainable AI (XAI).

Here are the top 50 research paper topics categorized by domain:

1. Large Language Models (LLMs) & Generative AI

1. Mitigating Hallucinations: A Comparative Study of Knowledge-Graph Integrated RAG vs. Vector-only RAG.

2. Agentic Workflows: Multi-agent Systems for Autonomous Financial Auditing and Report Generation.

3. Efficiency: Optimizing State Space Models (Mamba) for Long-context Medical Record Summarization.

4. Small Language Models (SLMs): Fine-tuning Phi-4 or Llama-Edge models for Offline Legal Document Analysis.

5. Prompt Engineering: Automated Prompt Optimization using Reinforcement Learning from Human Feedback (RLHF).

6. Multimodal LLMs: Cross-modal Retrieval between Histopathology Images and Clinical Notes.

7. Instruction Tuning: Impact of Low-Rank Adaptation (LoRA) on Domain-Specific LLM Performance.

8. Synthetic Data: Evaluating the "Model Collapse" Phenomenon in LLMs Trained on Generative Data.

9. Personalization: Developing Privacy-Preserving LLM Wrappers for Personalized Education Tutors.

10. Toxicity: Real-time Detection of Implicit Bias in Multi-lingual Generative AI Outputs.

2. Computer Vision & Generative Media

1. Deepfake Forensic: Diffusion-based Artifact Detection in High-Fidelity Synthetic Videos.

2. Medical Imaging: 3D UNet++ for Automated Volumetric Segmentation of Brain Tumors.

3. Edge Vision: Light-weight Vision Transformers (ViT) for Real-time Traffic Anomaly Detection on IoT Devices.

4. Action Recognition: Human Activity Recognition (HAR) using Gated Recurrent Units (GRU) on Skeleton Data.

5. Image Synthesis: Zero-shot Image-to-Image Translation using CycleGANs for Augmenting Rare Disease Datasets.

6. Remote Sensing: Multi-temporal Satellite Image Analysis for Real-time Drought Prediction using Vision Transformers.

7. Privacy: Federated Learning for Face Recognition without Centralized Data Storage.

8. Explainable Vision: Grad-CAM vs. LIME: Comparing Interpretability of CNNs in Mammogram Classification.

3. Healthcare & Bioinformatics

1. Predictive Analytics: Early Detection of Parkinson’s Disease using Longitudinal Voice and Gait Data.

2. Drug Discovery: Graph Neural Networks (GNN) for Predicting Protein-Ligand Binding Affinities.

3. Personalized Medicine: Multi-omics Data Integration for Cancer Survival Prediction.

4. Mental Health: Sentiment Analysis of Speech Patterns for Early Screening of Clinical Depression.

5. Clinical Decision Support: Uncertainty Quantification in Deep Learning Models for ICU Mortality Prediction.

6. Genomics: Transformer-based Models for DNA Sequence Classification and Motif Discovery.

7. Wearables: Non-invasive Blood Glucose Estimation using PPG Signals and Deep Recurrent Networks.

4. Finance & FinTech

1. Fraud Detection: Graph-based Anomaly Detection in Decentralized Finance (DeFi) Transactions.

2. Trading: Deep Reinforcement Learning for Multi-asset Portfolio Optimization in Volatile Markets.

3. Credit Scoring: Explainable AI (XAI) Frameworks for Mitigating Gender Bias in Loan Approval Systems.

4. NLP in Finance: Impact of Macroeconomic News on Crypto-volatility: A Transformer-based Sentiment Approach.

5. ESG Analytics: Automated ESG (Environmental, Social, Governance) Scoring of Corporations using Web-scraped Data.

6. Risk Management: Value-at-Risk (VaR) Prediction using Hybrid CNN-LSTM Models.

7. Anti-Money Laundering (AML): Temporal Graph Networks for Identifying Circular Transaction Patterns.

5. Cybersecurity, Privacy & Ethics

1. Adversarial ML: Robustness of Transformers against Adversarial Attacks in Network Intrusion Detection.

2. Differential Privacy: Balancing Utility and Privacy in Federated Learning for Financial Institutions.

3. Ethics: Developing Metrics for Measuring Data-Centric Fairness in Automated Hiring Systems.

4. Blockchain: A Secure Framework for Verifiable AI Model Training Logs using Blockchain.

5. Data Privacy: Synthetic Tabular Data Generation for GDPR-compliant Research Sharing.

6. Deepfake Ethics: Framework for Watermarking AI-generated Content to Prevent Identity Theft.

6. Edge AI, IoT & Smart Cities

1. TinyML: Energy-efficient Gesture Recognition for Smart Wearables using Quantized Neural Networks.

2. Smart Grid: Predictive Maintenance of Power Transformers using IoT Sensors and Random Forest.

3. Logistics: Dynamic Route Optimization in Smart Cities using Real-time Traffic and Weather Data.

4. Waste Management: AI-powered Vision Systems for Automated Waste Sorting and Recyclability Prediction.

5. Industrial IoT: Digital Twin Framework for Anomaly Detection in Manufacturing Assembly Lines.

6. Environment: Real-time Air Quality Index (AQI) Forecasting using Spatio-Temporal Graph Convolutional Networks.

7. Emerging Trends & Theory

1. Quantum Data Science: Exploring Quantum Support Vector Machines (QSVM) for High-dimensional Data.

2. Graph Analytics: Community Detection in Massive Social Networks using Parallelized Louvain Algorithms.

3. Active Learning: Reducing Labeling Costs for Rare Event Classification in Astronomy.

4. Physics-Informed ML: PINNs (Physics-Informed Neural Networks) for Modeling Climate Change Dynamics.

5. Recommender Systems: Context-aware Hybrid Recommendation Engines for Niche E-commerce Platforms.

6. Time-Series: A Comparative Analysis of Neural Basis Expansion Analysis (N-BEATS) vs. Traditional ARIMA.