Background: Machine learning has increasingly become a foundational tool in domains where uncertainty, optimization, and risk-sensitive decision making are central. Two such domains are residential and distributed energy management and health-related predictive analytics, particularly cardiovascular disease and cancer-oriented prediction systems. Although these fields are often studied separately, the references provided for this article reveal strong conceptual parallels. Both domains rely on forecasting, classification, prioritization, resource allocation, and intelligent support under uncertainty. In energy systems, machine learning and optimization are used to size photovoltaic-battery energy storage systems, schedule household loads, manage tariffs, and improve distributed energy use. In healthcare, machine learning is used to predict cardiovascular risk, classify disease states, and support diagnostic interpretation.

Objective: This study develops a publication-ready comparative research article that synthesizes the provided literature into a unified conceptual framework for predictive intelligence across residential energy systems and disease prediction environments. The goal is to identify shared principles, methodological convergences, domain-specific differences, and implications for future intelligent decision systems.

Methodology: A qualitative integrative research design was adopted using only the references supplied in the prompt. The literature was grouped into two principal domains: AI-enabled energy management and machine learning-based disease prediction. A structured thematic synthesis was then conducted across model design, optimization logic, prediction goals, operational uncertainty, personalization, cost sensitivity, and deployment implications.

Results: The analysis shows that both domains are shaped by a common architecture of predictive intelligence: data acquisition, model construction, uncertainty management, constrained optimization, and decision support. Energy studies emphasize storage sizing, tariff-aware scheduling, photovoltaic-battery coordination, and techno-economic trade-offs. Healthcare studies emphasize risk stratification, disease classification, hybrid modeling, and clinically relevant prediction. Despite different application contexts, both literatures converge around personalization, multistage decision support, hybrid intelligence, and the need to balance predictive accuracy with practical usability.

Conclusion: The study argues that residential energy management and disease prediction should be understood as structurally related forms of intelligent decision engineering. Future research should prioritize explainable, adaptive, human-centered, and cost-aware predictive systems capable of operating reliably in real-world settings.