Purpose: The exponential growth of complex semiconductor architectures, particularly for IoT, AI, and mobile computing, has made power consumption the primary design constraint. Low-power design techniques (LPDTs) like Dynamic Voltage and Frequency Scaling (DVFS), power gating, and clock gating, introduce significant verification challenges that traditional methodologies cannot adequately address. This article analyzes the existing "verification gap" and proposes an integrated methodological framework.

Methodology: This work conducts a comprehensive methodological review of current and emerging verification strategies. It analyzes the limitations of the standard Universal Verification Methodology (UVM) and conventional Design for Test (DFT) in low-power contexts. We then synthesize a novel framework integrating advanced UVM strategies (UVM-LP) with Artificial Intelligence (AI) and Machine Learning (ML) driven analytics.

Findings: The analysis indicates that standard UVM struggles with the state-space explosion of power domains and transitions. AI-driven approaches, including predictive analytics for test generation and active learning for power state analysis, show significant potential to optimize verification efforts, enhance coverage of critical corner cases, and reduce time-to-market. The synergy between UVM's structured environment and AI's intelligent optimization provides a robust solution.

Originality/Value: This article presents a holistic, integrated framework for low-power verification. It bridges the gap between structured verification (UVM) and intelligent automation (AI), offering a forward-looking perspective on managing the immense complexity of modern System-on-Chip (SoC) low-power design verification.