This research proposes an integrated recommendation framework to support automobile purchase decisions by aligning consumer preferences with spatial and technical factors. In response to the challenges faced by car buyers, particularly those with limited automotive expertise and overwhelmed by excessive information, the model leverages sentiment analysis from social media and combines it with spatial variables, including dealership density, vehicle availability, circulation restrictions, environmental suitability, and pricing. These factors are then matched with technical specifications to generate personalized and context-aware vehicle suggestions. Using Mexico City as a case study, the empirical analysis reveals that regional differences in infrastructure, regulatory policies, economic conditions, and environmental constraints strongly shape vehicle preferences. The findings show that consumers in areas with stricter vehicle regulations and better service infrastructure tend to prefer vehicles with higher initial costs but lower long-term maintenance demands. While the system is built around the socio-economic and infrastructural peculiarities of Mexico City, it has been designed with modular and configurable components, allowing it to be adapted to international contexts with similar data availability. Nevertheless, the dependence on locally specific variables should be explicitly acknowledged when considering its broader applicability. This raises the question of scalability and transferability, inviting further research into how the framework can be generalized across diverse urban and regulatory environments.