In order to predict stock values, this study investigates the combination of machine learning with sentiment analysis. The quick spread of news and the growth of social media sites like Twitter have made public opinion a bigger factor in financial markets. This study extracts market sentiment from tweets and news stories using Natural Language Processing (NLP) techniques, namely the VADER sentiment analysis tool, which has been tailored with financial lexicons. To forecast stock price fluctuations for firms like Amazon and Tesla, sentiment data is included into a Generative Adversarial Network (GAN) model together with technical indicators like moving averages and Bollinger Bands. The model is evaluated using performance metrics like Root Mean Square Error (RMSE), demonstrating its ability to capture price trends and market sentiment dynamics. While results highlight the potential of GANs for real-world applications in financial trading, the study also acknowledges limitations such as data quality and model uncertainty. Future directions include improving sentiment algorithms and incorporating additional market factors.