Stock market trend forecasting is difficult due to its complexity and volatility. This work uses Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Generative Adversarial Network algorithms predict India’s Nifty50 NSE Index Stock Price. The 16-year dataset includes ‘Open,’ ‘High,’ ‘Low,’ ‘Volume,’ ‘Adjacent Close,’ and a rolling 50-day average of ‘Close’. Cell state-based LSTMs capture long-term data dependencies well. GRUs, like RNNs, use reset and update gates to adapt quickly to new data with fewer parameters. GANs generate synthetic data using a generator-discriminator architecture, promising stock price prediction. Research begins with data preprocessing, creating sliding window sequences of historical prices and rolling averages. LSTM and GRU models predict prices using this data. The GAN predicts realistic prices using its unique architecture. Training and testing model predictions are compared to prices and rolling average trends. Results show each algorithm’s predictive power. LSTMs help short-term predictions by capturing complex temporal dependencies. Competitively efficient and accurate, GRUs perform well. GANs are innovative but can be refined. These algorithms improve trading strategies, but the study acknowledges stock market prediction’s complexity and uncertainty. The need for on-going research, new methods, and collaboration is stressed to improve prediction accuracy and effectiveness.