Air pollution poses significant threats to public health and the environment, necessitating effective prediction and control strategies. This paper presents an innovative model that combines K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN) to enhance the energy efficiency of air pollution prediction and control systems. The KNN algorithm is a robust tool for identifying similar historical data points, facilitating accurate forecasting of pollution levels based on various environmental factors. Complementing this, the ANN captures complex nonlinear relationships within the data, allowing for improved decision-making in pollution control measures. Our approach leverages a dataset comprising real-time pollution metrics, meteorological data, and energy consumption statistics. The model accurately predicts air quality indices through rigorous training and validation while optimizing energy usage. Evaluation results indicate that integrating KNN with ANN enhances predictive performance and promotes sustainable practices by minimizing energy expenditure in monitoring and controlling air pollution. This research contributes to developing smarter, more efficient environmental management systems, paving the way for healthier urban living environments.