Optimizing materials and energy efficiency in infrastructure is crucial for sustainability, cost reduction, and environmental impact mitigation. This research introduces SVC-Plus, an advanced Support Vector Classification (SVC) model, designed to enhance predictive accuracy and decision-making in material selection and energy optimization. The model incorporates kernel function optimization, advanced hyperparameter tuning, ensemble learning, and adaptive support vector refinement, significantly improving classification performance. To ensure interpretability, advanced AI systems are integrated, allowing stakeholders to understand the model’s decision-making process. The study evaluates SVC-Plus against traditional classifiers identifying the problems of those models in this field. The dataset, sourced from infrastructure databases, energy monitoring systems, and material efficiency reports, includes 1000 samples with key features (Energy Consumed, Production Output, and Efficiency Class, smart building sensors, and sustainability reports) with a balanced industry distribution. Missing values are handled via interpolation and imputation, numerical features are standardized, categorical features are encoded using one-hot and label encoding, and feature scaling is applied using Min-Max scaling for consistency. A genuine deployment model is proposed to enable real-time decision-making, leveraging IoT integration for continuous monitoring and optimization. The results demonstrate superior classification accuracy (99.33%), precision (0.9933), and recall (0.9934), along with the robustness, and adaptability compared to conventional models. This study bridges the gap between machine learning advancements and sustainable infrastructure management, providing an intelligent, scalable, and interpretable solution for optimizing resource efficiency in the built environment. Hybrid deep learning models, transfer learning, real-time sensor data integration, and multi-objective optimization will be considered, ensuring scalability and adaptability across diverse infrastructure domains.