This study presents a novel sales forecasting approach for Mahram Food Industries, combining technical analysis, time series modeling, machine learning, neural networks, and random forest techniques to enhance sales prediction accuracy. The research evaluates the performance of a neural network against traditional methods such as multiple variable regression and time series modeling using metrics like Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE). The neural network outperformed these methods, achieving a MAD of 28.33, MADP of 10.2%, and MSE of 6452, indicating superior accuracy in sales forecasting. The study highlights the effectiveness of integrating advanced techniques in improving sales forecasting for Mahram Food Industries. The comprehensive approach, which includes data collection, model selection, training, and evaluation, provides valuable insights into influential features and addresses issues such as overfitting and model complexity. The study also emphasizes the importance of understanding the business context and aligning data science efforts with business goals. Future research could benefit from incorporating a broader array of data sources and exploring dynamic model updating mechanisms to enhance predictive capabilities. The study underscores the value of predictive analytics in enhancing decision-making, inventory management, marketing efforts, and resource allocation. The research contributes to the field of sales forecasting by demonstrating the potential of machine learning techniques in improving accuracy and supporting informed business decisions.This study presents a novel sales forecasting approach for Mahram Food Industries, combining technical analysis, time series modeling, machine learning, neural networks, and random forest techniques to enhance sales prediction accuracy. The research evaluates the performance of a neural network against traditional methods such as multiple variable regression and time series modeling using metrics like Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE). The neural network outperformed these methods, achieving a MAD of 28.33, MADP of 10.2%, and MSE of 6452, indicating superior accuracy in sales forecasting. The study highlights the effectiveness of integrating advanced techniques in improving sales forecasting for Mahram Food Industries. The comprehensive approach, which includes data collection, model selection, training, and evaluation, provides valuable insights into influential features and addresses issues such as overfitting and model complexity. The study also emphasizes the importance of understanding the business context and aligning data science efforts with business goals. Future research could benefit from incorporating a broader array of data sources and exploring dynamic model updating mechanisms to enhance predictive capabilities. The study underscores the value of predictive analytics in enhancing decision-making, inventory management, marketing efforts, and resource allocation. The research contributes to the field of sales forecasting by demonstrating the potential of machine learning techniques in improving accuracy and supporting informed business decisions.