There has been a scarcity of comprehensive studies that aim to predict both the levels of PM2.5 particles and their corresponding impacts on human health. To fill this gap, we developed an artificial neural network model and AirQ+ software to predict PM2.5 concentrations and assess their associated health effects. The ANN model utilized five distinct input parameters, specifically wind velocity, wind orientation, atmospheric temperature, relative moisture content, and PM2.5 levels, observed within the time frame of 2018 to 2019. The concealed stratum comprised a total of ten neurons, in addition to the presence of an output layer. The MLP neural network demonstrated strong correlations at each stage: 0.908 (training), 0.910 (validation), 0.914 (testing), and 0.907 overall. The RMSE was determined as 6.52 µg/m3 when evaluating the neural network, thus indicating the notable predictive precision exhibited by the multilayer perceptron (MLP) neural network when forecasting the concentration of PM2.5 particles. The AirQ+ software, created by the World Health Organization (WHO), was employed to assess the magnitude and consequences of PM2.5 concentrations. The average concentration of PM2.5 particles throughout the duration of the study was recorded as 26.5 μg/m3, which exceeds the recommended limit provided by the WHO by a factor of 5.3. The study estimated the proportions and numbers of deaths attributed to various conditions. Specifically, chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), stroke, and all-cause mortality accounted for approximately 11.67%, 15.02%, 13.25%, 15.225%, and 9.45% respectively. The estimated number of deaths were 19,195 for all causes, 10,063 for COPD, 564 for IHD, and 1,063 for lung cancer. The findings of this investigation demonstrate a high degree of reliability in the methodologies utilized. Through the utilization of these approaches, individuals in positions of authority and responsibility can aptly evaluate the cost-benefit analysis, leading to a reduction in human casualties and mitigating the economic burdens on society.