Climate change has significantly reshaped energy supply and demand by increasing the frequency and severity of extreme events, thereby intensifying challenges related to the sustainability and security of energy systems. In response, artificial intelligence has emerged as a critical enabling technology with the potential to improve forecasting accuracy, operational flexibility, and system resilience under non-stationary climatic conditions. Nevertheless, the future integration of AI into energy systems is subject to profound uncertainties arising from climatic dynamics, governance structures, and policy environments. This study investigates plausible futures of AI-enabled climate-responsive energy systems in Iran through a scenario-based approach. The research adopts an applied mixed-methods design. Initially, key drivers influencing the future development of climate-responsive energy systems were identified through a systematic literature review and expert interviews. Subsequently, Cross-Impact Analysis using the MICMAC method was employed to examine the influence–dependence relationships among these drivers. The analysis identified climate and energy security pressures and environmental and market-based policy stringency as the dominant exogenous drivers shaping system evolution. Based on the alternative states of these two critical uncertainties, four plausible future scenarios were constructed. The results indicate that under high climate pressure, strong and coherent governance combined with market-oriented environmental policies can convert stress into a driver of structural transformation and systematic AI deployment. Conversely, weak policy frameworks even when climate pressures are moderate lead to missed opportunities and the accumulation of long-term vulnerabilities. Overall, the study highlights that managing uncertainty through effective governance and strategic AI adoption is central to achieving resilient energy futures.