Smart Personal Expense Tracker with Predictive Analytics using Machine Learning

Personal Finance management has become important role in modern digital lifestyles, where each individual frequently struggles to track and predict their spending behavior. Traditional expense-tracking use focuses primarily on manual logging and static charts, offering limited predictions intelligence. Through this research we propose a Smart Personal Expense Tracker that integrates Machine Learning (ML) to provide automatic expense categorization, time-series forecasting, and personalized spending insights. The system combines a user -friendly web interface with backend ML models such as Random Forest, LSTM, Prophet, and OCR-based NLP Modules. Experiment show result which demonstrates that predicative analytics can effectively forecast next – month, how much expenditure will be happened. It also offer alerts for potential overspending. The models improve financial awareness and improve decision making. The system is scalable, deployable, and useful for personal finance management.