The increasing number and diversity of connected devices in IoT applications make them dynamic and unpredictable. The presence of new devices and the removal of existing ones may lead to variations in device availability and characteristics. Due to the heterogenity of resources, requirements of users become more dynamic and the provisioning of resources also becomes challenging. Especially in microservice-based IoT applications, systems are highly distributed and heterogeneous, consisting of a wide variety of devices and services with differing capabilities and requirements. Static resource allocation approaches, which allocate resources based on predefined rules or fixed configurations, may not able to adapt to these dynamic changes. Conventional static resource allocation approaches are inadequate for large-scale IoT systems due to lack context awareness. This paper presents an approach that integrates context-awareness for dynamic resource provisioning using reinforcement learning in microservice-based IoT systems. The system optimize resource allocation strategies by considering contextual factors such as device properties, functionalities, environmental conditions, and user requirements. Integrating reinforcement learning allows the framework to constantly learn and adjust its resource provisioning methods, resulting in better performance and resource reuse. The experimental analysis demonstrates the effectiveness of the framework in optimizing resource utilization, improving system efficiency, and enhancing overall performance. The study highlights the potential of machine learning mechanisms to further optimize resource utilization and emphasizes the importance of future research to analyze the scalability, robustness, and overall performance of context-aware resource provisioning.