Stock prediction and portfolio management are vital in the financial industry, aiding investors in informed decisions. This study introduces a novel approach termed the Stock Prediction and Portfolio Management Model, which amalgamates advanced machine learning algorithms with portfolio optimization techniques. The Stock Prediction and Portfolio Management Model framework substantially enhances the precision of stock price predictions, leading to an elevated performance of investment portfolios while concurrently mitigating inherent risks. The initial phase of the model focuses on predicting stock prices by harnessing historical data and employing technical indicators like the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD). In the subsequent phase, the SPPMM system meticulously constructs optimal portfolios utilizing genetic algorithms, further solidifying its efficacy in aiding investors in making informed decisions.