With the rapid expansion of digital transactions, especially through the Unified Payments Interface (UPI), the need for automated tracking and intelligent financial monitoring has become more relevant than ever. Modern users engage in frequent digital purchases—ranging from daily groceries to transportation—leading to an overload of unorganized transactional data in banking applications. Although banks provide statements and notifications, users often lack structured insights about their spending patterns. This paper introduces the AI UPI Budget Analyzer, an intelligent web-based system designed to automate the categorization of UPI transactions, monitor personal budgets, and generate meaningful financial insights in real time. The system integrates QR code scanning for instant transaction capture, uses a rule-based keyword matching algorithm for accurate expense classification, and presents insights through interactive dashboards and visual analytics. Users receive real-time alerts during overspending, along with a calculated financial health score and personalized spending trends. The system is implemented using React.js for the frontend, Node.js for backend logic, and SQLite for lightweight data storage. The overall architecture ensures scalability, modularity, and efficiency. Experimental evaluation shows that the integrated AI engine achieves high accuracy in categorizing transactions with minimal latency. The findings demonstrate that the AI UPI Budget Analyzer can significantly improve personal expenditure management, reduce manual workloads, and promote healthier financial practices.

KEYWORDS— AI categorization, UPI transactions, budget monitoring, financial analytics, expense tracker, rule-based algorithm.