The automotive industry faces constant challenges in accurate demand prediction due to market volatility, macroeconomic fluctuations, and the complexity of consumer purchasing decisions. This project develops a comprehensive forecasting system for all Ford vehicle lines and their respective catalogs, implementing an advanced machine learning methodology that integrates endogenous and exogenous variables to generate 12-month sales predictions with price sensitivity analysis. The primary objective is to develop a robust predictive model capable of forecasting sold units for each Ford vehicle catalog under different price variation scenarios, providing strategic insights for commercial and pricing decision-making. The system enables evaluation of price increases or decreases impact on projected demand, facilitating pricing strategy optimization and revenue maximization. The model architecture incorporates a hybrid approach combining time series, neural networks, and advanced machine learning algorithms. Endogenous variables include historical sales time series, capturing seasonal patterns, trends, and cycles inherent to each product line. Exogenous variables comprise: current and projected inventory, proprietary promotions (historical and planned), competitor promotional activities, weighted prices reflecting retail-fleet mix, foreign exchange rate penalties, and relevant macroeconomic indicators such as GDP, inflation, interest rates, and consumer confidence. The model implements ensemble learning techniques that optimize the combination of different algorithms to minimize mean squared error (MSE). Recurrent neural networks (LSTM/GRU) are utilized to capture complex temporal dependencies, ARIMA-X models for time series components, and gradient boosting algorithms to model non-linear relationships between explanatory variables. The architecture includes specialized modules for price-demand elasticity analysis, enabling scenario simulations under different pricing strategies. The solution provides granular forecasting capabilities at individual catalog level, facilitating differentiated strategic decisions by segment. The price sensitivity analysis system allows evaluation of volume-margin trade-offs, optimizing commercial strategy. Integration of macroeconomic and competitive variables ensures predictions reflect the Mexican automotive market context. Significant improvement in forecasting accuracy is anticipated compared to traditional methods, reducing uncertainty in production planning, inventory management, and commercial strategies. The system will contribute to resource optimization, inventory cost reduction, and market opportunity maximization through evidence-based dynamic pricing.