The accurate prediction of sales forms the basis of business analytics, which helps organizations make better decisions for their inventory, resource planning, and strategic choices. Statistical and regression forecasting techniques commonly provide insufficient accuracy in detecting complex non-linear sales data patterns. The research analyzes the effectiveness of Decision Trees, Random Forests, XGBoost, and Long Short-Term Memory networks as machine learning models for improving forecasting precision. The forecasting process requires systematic work, from data acquisition and cleaning, followed by feature transformation until model selection and performance assessment with MAPE and RMSE metrics. Research findings prove that ML ensemble and deep learning models exceed traditional forecasting standards because they successfully deal with extensive data sets and maintain performance in altering market environments. Sales prediction accuracy benefits substantially from including additional external variables that include economic metrics and data from consumer sentiment. These findings present essential concepts regarding ML’s power in sales forecasting while delivering concrete guidance to organizations that need data-based decision support systems. Future studies will develop real-time learning approaches to enhance the predictive results.