Strategic resource allocation in renewable energy engineering projects is characterized by high uncertainty, multi-objective trade-offs, and dynamic environmental and market conditions. Traditional planning and optimization approaches often rely on static assumptions, centralized decision-making, and deterministic models that fail to adapt to real-time fluctuations in energy generation, demand patterns, weather variability, and grid constraints. Recent advances in artificial intelligence (AI) enable the development of smart decision support systems (DSS) capable of learning from data, anticipating uncertainty, and optimizing resource deployment across the lifecycle of renewable energy projects.

This paper presents a conceptual and analytical framework for AI-powered decision support systems designed to enhance strategic resource allocation in renewable energy engineering. The proposed framework integrates machine learning, predictive analytics, optimization algorithms, and multi-criteria decision analysis to support planning, scheduling, investment prioritization, and operational control across solar, wind, hydro, and hybrid energy systems. The study positions AI-driven DSS not merely as optimization tools but as cognitive infrastructures that enable adaptive, data-driven, and resilient decision-making. The paper synthesizes existing literature and proposes structural layers, decision metrics, and system-level implications, providing a foundation for future empirical validation and real-world implementation..