The stock market serves as a crucial financial platform where individuals and institutions engage in trading to achieve economic growth and portfolio expansion. Beyond merely buying and selling shares, the stock market encompasses various dimensions, including advanced data analytics and fraud detection. Stock markets are vulnerable to manipulation due to complex trades, advanced fraud techniques, and limited oversight, especially in emerging markets. To address this, we propose a Machine Learning-based solution that classifies fraudulent activities, trains models for accurate detection, and integrates predictive algorithms into a web application with an alert system for enhanced trade security. This study focuses on stock shares within the IT industry, particularly companies like Google, NVIDIA, Tesla High-Performance Computers, and Amazon. The objective of this research is to develop a comprehensive system for analyzing and monitoring stock market trades by integrating historical and real-time data. A key aspect of this study is identifying fraudulent trading activities through pattern recognition using Machine Learning (ML) algorithms and statistical techniques. The system aims to enhance trading security while also providing users with analytical tools, visual insights, and comparative reports to facilitate informed decision-making. Additionally, leveraging ML for stock market prediction enhances trading efficiency by utilizing computational power for accurate trend analysis. A significant research gap lies in developing robust fraud detection methodologies, requiring advanced data analysis techniques and efficient data capture mechanisms. By addressing these challenges, this research contributes to the development of a reliable stock market monitoring system that ensures security, transparency, and data-driven decision-making.