This study investigates the dynamic temporal relationship between climate policy uncertainty (CPU) and international energy prices across nine commodities from January 1992 to June 2024. The research examines whether CPU systematically drives energy price movements and how these relationships evolve over time. We employ dynamic time warping (DTW), a nonparametric pattern recognition technique that accommodates non-linear temporal alignments between time series. Unlike conventional econometric methods that impose fixed lag structures, DTW flexibly maps how CPU influences on energy prices evolve across different periods. We analyse crude oil (WTI and Brent), gasoline, heating oil, coal, liquefied natural gas, natural gas, palm oil, and sunflower oil using multiple DTW step patterns (Rabiner–Juang VI-c, Symmetric1) with Sakoe–Chiba window constraints. Lead–lag analysis quantifies quarterly shifts in temporal precedence between CPU and energy prices. Energy prices demonstrate increasing sensitivity to CPU, particularly after the early 2000s. Traditional fossil fuels show pronounced alignment with CPU during major policy shifts, including the post-2008 financial crisis and 2015 Paris Agreement. CPU often leads energy price movements during regulatory transitions, suggesting markets price anticipated policy risks. Recent years reveal temporal reversals: natural gas and coal prices increasingly lead CPU, indicating market dynamics now drive subsequent policy adjustments as energy transitions accelerate. This research introduces sophisticated temporal analysis using DTW and lead–lag methods to explore evolving CPU-energy price relationships. The study provides fresh insights into how climate-related regulations drive energy market volatility during transitions toward sustainable energy systems, with implications for portfolio management and regulatory design.