This research presents the development of an intelligent stock recommendation system that utilizes advanced machine learning models for informed long-term investment decisions. The system addresses the complexities of the stock market, where traditional methods often fall short in accessibility, accuracy, and efficiency. By automating fundamental analysis with models like Long Short-Term Memory (LSTM) networks and the CNN-GRU-XGBoost hybrid model, the system integrates key financial ratios, macroeconomic indicators, and sector performance, providing data-driven insights. The proposed framework optimizes stock selection using XGBoost and forecasts future stock prices with LSTM, offering precise and scalable solutions for diverse investment portfolios. The literature review highlights modern methodologies like TRAN, Bi-LSTM, and hybrid models, which improve stock forecasting and trading strategies by incorporating temporal dependencies and inter-stock relationships. The algorithmic analysis explains LSTM’s ability to handle sequential data and the hybrid model’s powerful feature extraction and prediction capabilities. This hybrid approach enhances decision-making, saves time, and democratizes financial insights, making advanced analysis accessible to individual investors, robo-advisors, and educational institutions. While offering benefits like scalability and reduced biases, the system also faces challenges, such as computational costs and market volatility. Backtesting results confirm the system’s adaptability to dynamic market conditions, ensuring sustainable investment strategies. This project showcases the transformative potential of AI/ML in financial analytics, laying a strong foundation for long-term, informed investment decisions.