Air pollution poses a significant health risk worldwide, with mortality rates from ambient particulate matter pollution increasing in many regions. This study focuses on forecasting air pollution-related mortality rates in two Central Asian cities, Bishkek (Kyrgyzstan) and Almaty (Kazakhstan). Utilizing time-series models, specifically Long Short-Term Memory (LSTM) networks and Prophet, the research aims to provide accurate predictions that can inform public health policies and interventions. The proposed methodology integrates advanced data preprocessing techniques, robust model architectures, and hyperparameter tuning to achieve an accuracy exceeding 85%. The findings reveal that time-series forecasting can effectively model the trend and seasonality of mortality rates, offering actionable insights for policymakers.