This research article presents a comprehensive study on using machine learning techniques to forecast sales for Mehram Food Industries. The study integrates technical analysis, time series modeling, machine learning, neural networks, and random forest methods to enhance the accuracy of sales forecasting. The primary goal is to predict future sales through a robust framework that captures complex, nonlinear relationships in sales data. The neural network model, specifically a Nonlinear Autoregressive model with exogenous input (NARX), is compared against traditional methods such as multiple variable regression and time series modeling. The evaluation metrics used include Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE). The results show that the neural network outperforms the traditional methods, with lower MAD (28.33), MADP (10.2%), and MSE (6452) values. The study highlights the superior performance of the neural network in sales forecasting, making it a valuable tool for informed decision-making in business management. The research also discusses the limitations of the study, such as the reliance on historical data and the need for broader data sources, and suggests future research directions to improve the model's applicability and interpretability.This research article presents a comprehensive study on using machine learning techniques to forecast sales for Mehram Food Industries. The study integrates technical analysis, time series modeling, machine learning, neural networks, and random forest methods to enhance the accuracy of sales forecasting. The primary goal is to predict future sales through a robust framework that captures complex, nonlinear relationships in sales data. The neural network model, specifically a Nonlinear Autoregressive model with exogenous input (NARX), is compared against traditional methods such as multiple variable regression and time series modeling. The evaluation metrics used include Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE). The results show that the neural network outperforms the traditional methods, with lower MAD (28.33), MADP (10.2%), and MSE (6452) values. The study highlights the superior performance of the neural network in sales forecasting, making it a valuable tool for informed decision-making in business management. The research also discusses the limitations of the study, such as the reliance on historical data and the need for broader data sources, and suggests future research directions to improve the model's applicability and interpretability.