The stock market forecasting of earlier machine and deep learning models was not very accurate since the models were not trained properly because of certain dataset difficulties. Much work has been done to develop efficient models for anticipating stock price changes. The intrinsic complexity and dynamic nature of the financial market industry served as the impetus for this investigation. This article presents a thorough analysis of the dynamic field of stock prediction using deep learning and machine learning techniques. The goal of this study is to improve stock market prediction accuracy by creating and assessing a hybrid forecasting model that combines the advantages of Long Short-Term Memory and AutoRegressive Integrated Moving Average models. This hybridization is driven by the complimentary properties of both LSTM, which is renowned for its capacity to represent complex long-term dependencies within financial time series data, and ARIMA, which is well-known for its skill in capturing short-term swings. By combining the benefits of these two distinct strategies, we seek to optimize their potential, reduce the disadvantages of employing individual models alone, and improve prediction performance. We also assess the hybrid model’s robustness, scalability, and computational efficiency in real-world implementation issues, especially under dynamic and uncertain market situations. By tackling these issues, we want to aid in the creation of a flexible and reliable forecasting instrument with useful applications in the dynamic world of financial markets.