In the field of Computer Science, Artificial Intelligence and Distributed Systems, cold-start latency spikes and memory over-provisioning impair serverless function execution in resource-constrained IoT edge environments. This empirical investigation systematically examines Reinforcement Learning-Based Proactive Container Autoscaling for Serverless Edge Computing through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing Q-learning warm-up scheduling deployed on a Kubernetes edge cluster executing heterogeneous event-driven microservices, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the proactive scheduler reduced function invocation cold-start delays by 68% and trimmed idle memory consumption by 32%. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for edge cloud infrastructure architects and serverless application developers. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.