This work explores machine learning models for sales forecasting and product recommendation systems using the ContosoRetailDW dataset. The study aims to improve prediction accuracy and recommendation effectiveness that supports data-driven decision-making. Sales Forecasting: The project uses machine learning algorithms such as Linear Regression, Decision Trees, Random Forests, Support Vector Regressor (SVR), and XGBoost to predict sales volumes. Data preprocessing includes handling missing values, encoding categorical features, and normalizing numerical data. Models are evaluated using standard performance metrics. Preliminary results show that feature integration significantly improves prediction accuracy. Product Recommendations: Two recommendation systems are developed. The first uses collaborative filtering, including Singular Value Decomposition (SVD), to predict user preferences based on previous interactions.