Accurately predicting stock prices remains a challenging task, primarily due to the volatile, dynamic, and non-linear nature of financial markets. This study introduces a robust deep learning-based framework designed to forecast the closing prices of companies listed on the Nifty 50 index. We explore and compare the predictive performance of several state-of-the-art neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), alongside two novel hybrid models: StockAugNet-f and StockAugNet-c. Historical stock data were retrieved from Yahoo Finance and preprocessed using Min-Max normalization. To effectively model temporal dependencies, we applied a sliding window technique for sequence construction during training. Model performance was assessed using key evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). To enhance usability and support real-time forecasting, we developed an interactive Streamlit-based application, allowing users to dynamically visualize model outputs. Experimental findings indicate that the GRU and RNN model give better accuracy over the hybrid models accuracy and resilience for the NIFTY 50 index stocks.