Edge computing has emerged as a critical technology for achieving low latency and real-time data processing, especially in applications with the Internet of Things (IoTs) and smart cities. With their lightweight and portable nature, containers provide an effective solution for deploying and managing edge applications. The Particle Swarm Optimization (PSO) algorithm, inspired by the collective behavior of bird flocks, offers an efficient approach to complex optimization problems by iteratively updating particle positions within a multidimensional solution space. PSO's low computational complexity and strong global search capability make it highly suitable for resource allocation and task scheduling in edge environments. This paper presents an adaptive scheduling strategy based on PSO. In this approach, a time-series-based Dynamic Request Prediction Algorithm (DRPA) anticipates request patterns to dynamically adjust container placement, minimizing response time. Concurrently, a threshold-based PSO (TPSO) optimizes container scheduling based on node load, enhancing resource utilization and reducing scheduling delays. Experimental results show that DRPA-TPSO effectively lowers latency, boosts resource utilization, and improves system stability in high-load, complex environments, outperforming traditional scheduling methods.