srjecsSRJECSSRJECS2788-9408https://doi.org/10.47310/srjecs.2025.v05i02.004A Thorough Examination and Forecasting Framework for Middle Eastern Air Pollution Prediction Using Long Short-Term Memory NetworksMohammedTaher AhmedDepartment of Computer Science, College of Computer Science and Mathematics, Tikrit University, Tikrit, IraqAir quality within the Middle East has become a major environmental and public health crisis because particulate matter exceeds World Health Organization (WHO) acceptable standards. The construction of Middle Eastern air pollution temporal relationships leads to improved predictions through the implementation of Long Short-Term Memory (LSTM) neural networks. Our investigation examines 15 regional urban centers by analyzing more than 450 data points gathered across 30 days which includes environmental measurements together with detailed readings of PM2.5, PM10, NO2, O3, CO and SO2 pollutants. The proposed LSTM model implements state-of-the-art hyperparameter optimization through a three-layer (128 layers, 64 layers, and 32 layers) architecture combined with L2 regularization (0.001) and dropout regularization (0.3). The prediction model outperforms daily forecasts through its sliding window method which trains the network across seven consecutive days. The Root Mean Square Error (RMSE) of 23.39 μg/m³, Mean Absolute Error (MAE) of 19.90 μg/m³ and Mean Absolute Percentage Error (MAPE) of 30.07% for PM2.5 prediction results demonstrate that LSTM outperforms Linear Regression and Random Forest models. Fine particulate matter in the area exceeds 90% from human-made sources while showing seasonal and inter-city differences in the data analysis. The research solves critical technical deficits through extensive cross-validation procedures together with detailed baseline model assessments and comprehensive model design descriptions. The study demonstrates that LSTM networks successfully identify complex temporal patterns in Middle Eastern air pollution data for public health and environmental monitoring applications. The research presents a solid prediction system for arid and semi-arid areas with their complex weather patterns and elevated human-generated pollution while advancing environmental informatics knowledge base.