A machine learning framework for uncertainty aware predictive optimization in smart environment and financial systems

This research presents a novel Machine Learning Driven Methodology for Predictive Optimization (ML-DMPO). The framework introduces a theoretically grounded integration of uncertainty-aware predictive analytics with constraint-aware prescriptive optimization through a differentiable joint optimization objective. Unlike traditional decoupled approaches, ML-DMPO propagates predictive uncertainties directly into decision quality assessments. The methodology was validated through dual case studies spanning smart environments and financial systems. In smart building energy management, ML-DMPO achieved 26.9% energy cost reduction while maintaining 98.7% constraint satisfaction. In dynamic portfolio optimization, the methodology delivered a Sharpe ratio of 0.92, representing a 26% improvement over the strongest competing baseline (Robust Optimization at 0.73) and a 61% improvement relative to the S&P 500 index baseline (0.57) with perfect sector concentration adherence. Computationally, the framework demonstrated practical scalability with inference latencies of 12.4 milliseconds for building control and 9.8 milliseconds for portfolio decisions, meeting real-time requirements while maintaining near-linear scaling with problem dimensionality. The warm-start strategy reduced optimization time by 62-70%, and cross-domain transfer learning experiments demonstrated 89-92% of domain-specific performance with only 30-40% of training time when adapting between domains. Statistical significance testing confirmed all performance improvements at p < 0.01 levels, with Model Confidence Set analysis identifying ML-DMPO as belonging to the superior model set for 97% of performance metrics across both domains. The research demonstrates that tight integration of predictive machine learning with prescriptive optimization yields substantial performance improvements over state-of-the-art baselines while maintaining operational constraints and enabling adaptive decision-making in dynamic environments. The framework’s domain-agnostic architecture, validated across two disparate application domains, establishes a new paradigm for developing intelligent decision-support systems that can navigate the complexities of modern cyber-physical and financial ecosystems. The open-source implementation facilitates adoption and further research, contributing to the advancement of data-driven optimization methodologies with practical applicability in high-stakes decision environments.