This study presents a novel approach to predicting stock prices by integrating a hybrid deep learning model with a comprehensive analysis of technical data. The methodology utilises Python and its associated modules to systematically acquire, process systematically, and model financial time series data. Historical stock data for blue-chip stocks from the Bombay Stock Exchange (BSE) is obtained, and various technical indicators, such as volume, volatility, trend, and momentum, are calculated. A hybrid deep learning architecture employing long-short-term memory (LSTMS) and a multi-head attention mechanism captures complex temporal correlations in the technical indicator data. The model’s predictive capability is comprehensively evaluated, and the findings illustrate the effectiveness of the proposed strategy in forecasting stock prices. The framework achieved a maximum R2 of 0.97 and a minimum MAPE of 1.67 for stock LT when the test data was predicted over 200 epochs with a learning rate of 0.001. Nonetheless, the framework achieved an accuracy of 89% when trained on 5000 trading days, exceeding the performance of those trained on fewer than 5000 days.