Media Mix Modeling (mMM) is a data-driven analytical approach that quantifies the influence of multiple advertising channels on sales performance while adjusting for external factors such as seasonality and economic conditions. This study explores machine learning-based mMM to predict weekly sales performance in the Sri Lankan home appliances industry by combining ad spending variables with external factors such as exchange rates and holidays. The study applies time series decomposition, ad stock transformation, and data normalization to capture delayed and nonlinear media effects. TimeSeriesSplit cross-validation was used to test four predictive models: Linear Regression, XGBoost Regression, Support Vector Regression (SVR), and Bayesian Ridge Regression, along with key metrics for performance (MSE, RMSE, R2 Score, MAPE). The tuned XGBoost model had the best accuracy (R2 = 0.9145; MAPE = 2.87%), making it most useful for predicting. The findings demonstrate the importance of machine learning-enhanced mMM as an effective strategy for optimizing media spending and increasing advertising efficiency in competitive, budget-constrained markets.