In the ever-evolving financial landscape, data science, and machine learning have become instrumental in shaping investment strategies. This study introduces the "Integrated Financial Analysis and Asset Recommendation" (IFAAR) platform, a revolutionary tool designed to optimize investment decisions. This system will employ advanced techniques in stock recommendation, utilizing technical indicators and the Prophet library for precise time-series forecasting. Covering stocks, cryptocurrencies, mutual funds, and physical assets, the platform offers a diverse array of investment options. A key innovation lies in the integration of feature normalization methodologies, proportionate allocation, and elevating prediction accuracy. This multidisciplinary platform combines finance, data science, and machine learning to dynamically allocate assets, adapting to changing economic landscapes and optimizing the risk-return balance. This system will empower users with tailored investment recommendations through a user-friendly interface. Predictions are grounded in a meticulous analysis of historical data patterns, sentiment analysis from news sources, the psychological behavior of investors, and the use of technical indicators like MACD, RSI, ARIMA, and LSTM. Contributing significantly to the financial industry, this research introduces a practical and valuable tool for investors of any expertise to navigate market complexities. IFAAR would quantitatively gauge the security stance of integrated packages, enhancing software security and mitigating potential risks