Monitoring methods are necessary because urban air pollution has an impact on increasing serious risks to human health and the environment. Nevertheless, these systems have drawbacks such as expensive infrastructure, scant monitoring, and insufficient real-time capability. Based on these limitations, this research proposes a comprehensive mobile computing-based monitoring system by integrating an Internet of Things (IoT) sensor network with a cross-platform mobile application. Development with the IONIC-Vue framework results in a system capable of 90% code reuse and a 40% reduction in development costs compared to the original approach, while maintaining native performance. A three-tier, microservices-based architecture supports real-time scalability capable of handling up to 100,000 sensor readings per minute with a latency of <30 s. Data is transmitted over MQTT using smart pole hardware that is outfitted with environmental, particulate matter, and CO2 sensors, guaranteeing dependable and effective communication. In addition to meeting system performance goals for responsiveness, energy efficiency, and data usage, the system offers an interface that complies with accessibility standards. In comparison to current options, evaluation results show cost-effectiveness, real-time monitoring, and an improved user experience. This research offers a scalable framework for future smart city air quality monitoring and predictive analytics by presenting a revolutionary cross-platform mobile computing architecture tailored for IoT sensor integration.