This study describes an integrated approach for forecasting the profitability of stock symbols on the ‘Palestine Stock Exchange’, which is a subset of stock exchanges with unique political and economic circumstances that make understanding, trending, and prediction more difficult than on other stock exchanges around the world. The proposed approach combines an effective data mining technique in conjunction with sophisticated technologies to make accurate predictions. SSIS (SQL Server Integration Services) is used to manage enormous datasets, including ten years of daily trade data from the Palestine Stock Exchange. The system integrates filtering and separation procedures to ensure accurate forecasts. Furthermore, to handle the data in time series form and build solid prediction models, it employs an ‘LSTM (Long Short-Term Memory)’, which is a type of the ‘RNN (Recurrent Neural Network)’ extensively used in machine learning, it considers a variety of factors, including the day and date, because the Palestine Stock exchange does not operate on Fridays or Saturdays, and also addresses economic difficulties such as delays in government employee salary disbursements, which can have an adverse effect on exchange trade. This solution enables investors to make well-informed purchasing and selling decisions by properly anticipating stock symbol prices, considering the complex factors unique to the Palestine Stock Exchange, this integrated system provides a comprehensive approach to stock prediction while also being adaptable for any new symbol addition because it is trained with big historical different symbols data and is also adaptable for any stock exchange prediction.