This study investigates the impact of competitor analytics on decision-making at a leather manufacturing company, using machine learning algorithms such as Random Forest, Support Vector Regression (SVR), and XGBoost. Trained on a dataset of 5,871 samples, the models were evaluated with Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). SVR emerged as the most efficient model. Python libraries NumPy and Pandas in Jupyter Notebook facilitated the data analysis. The SVR model provided valuable insights for strategic decision-making and competitor performance predictions. This study underscores the importance of competitor analytics and machine learning in enhancing manufacturing strategies. Keywords: competitor analytics, machine learning algorithms, strategic decision making, predictive modeling, python, data science