A novel methodological approach was developed using statistical data analyses and microclimate modelling to understand and predict the environmental impacts on health responses associated with extreme heat and air quality and the benefits of enriching urban greenery cover and increasing surface albedo. Microclimate simulations and statistical analyses were developed to assess the effects of heat and air pollution mitigation strategies on pollutant removal ability, reducing outdoor heat stress, and influencing human health. The statistical models were developed by developing a novel technique for log-linear Poisson regression based on clustering factors to increase model fit. The statistical models were utilized based on historical datasets to predict emergency department visits and fine particulate matter based on changes in outdoor heat stress. The results confirmed significant reductions in outdoor heat and predicted reductions in health records and air quality levels associated with implementing heat and air quality mitigation scenarios. The proposed framework contributes to building climatic resilience and supports urban development decisions to expand natural infrastructure and enact climate adaptation policy.