The construction sector is increasingly compelled to adopt sustainable methodologies due to the intensifying challenges of energy overconsumption and environmental degradation. Given that buildings account for a substantial share of global energy demand, there is a critical need for data-centric strategies to enhance design efficiency and reduce environmental impact. This study presents a computational framework that integrates artificial intelligence (AI) with parametric design to optimize key design parameters, specifically building orientation, window opening side, and material selection for energy efficient and climate responsive construction. Drawing on environmental datasets from over 150 diverse locations across India, the system employs supervised machine learning algorithms to deliver predictive recommendations adapted to regional climatic conditions. These models are embedded within a Building Information Modeling (BIM) environment, enabling performance driven simulations. The proposed approach achieves up to a 40 percent reduction in energy usage by aligning design strategies with localized environmental data. By incorporating quantifiable sustainability metrics and addressing regional variability, this study demonstrates the transformative potential of AI in promoting energy efficient and environmentally responsible built environments.