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Climate change exacerbates extreme heat events in the North Africa and Arabian Peninsula (NAAP) region, posing a significant threat to human health and well-being. This study proposes a novel approach for reliable mapping of population exposure in NAAP to different UTCI levels under future climate change scenarios by improved prediction of UTCI using machine learning (ML) methods. Daily mean UTCI was calculated using hourly ERA-HEAT data from 1979 to 2022. Sobol's global sensitivity analysis was used to identify the most influential meteorological variables affecting UTCI. Three ML models were trained to predict monthly UTCI based on these variables. The best-performing model was used to generate historical and future projections of UTCI for four CMIP6 climate models under Paris Agreement goal scenarios. Finally, the projected change in population exposure to mean UTCI was assessed by comparing historical and future projections. The results showed a significant influence of air temperature, wind speed, surface pressure, solar radiation, and humidity on UTCI in NAAP. Estimation of UTCI using these variables showed a higher performance of random forest (RF) (Kling-Gupta Efficiency = 0.84) compared to other models. The projection of UTCI from CMIP6 climatic model simulations using the RF model revealed a decrease in UTCI for the 1.5 °C temperature rise scenario by 0.25–0.75 °C, while an increase by 0.25–0.75 °C for the 2.0 °C temperature rise scenario in most of NAAP. The study revealed that even a modest increase of 0.5 °C in global warming could lead to a 20-day increase in the number of days with moderate thermal stress in Egypt and Sudan and a significant increase in the number of days with strong thermal stress in the Arabian Peninsula. The population exposure to heat stress would increase significantly even under the most ambitious Paris Agreement goals. These findings highlight the urgent need for adaptation and mitigation strategies to protect public health and well-being in the NAAP region from escalating risks.