This review examines an array of machine learning algorithms applied to optimize the performance of the WSN sensors concerning vital metrics such as remaining energy reserves, node operational lifespan, and overall energy usage for air pollution monitoring systems. Additionally, the review underscores the value of integrating reinforcement learning approaches and Arduino-based technologies to effectively monitor carbon dioxide, nitrogen dioxide, and ozone pollutants. The study also surveys diverse energy-efficient models like the Traffic-Balanced Energy-Efficient Zone-Based Multipath (TEZEM) method, Cluster-Based Load-Balanced Routing with Power Awareness (CLWA) algorithm, Pressure-Driven Energy-Aware Geographic Forwarding (PEG-GAS), and Intelligent Energy-Efficient LEACH (IEE-LEACH) protocol, comparing their potencies based on residual battery power and active component counts. Furthermore, potential real-world deployments within an urban environment in Tasikmalaya, Indonesia, are examined, underscoring the advantages of developing air quality benchmarks and wireless sensor network detection systems.