Serverless enables users to execute the code and shift the management problem towards Cloud Service Providers. Resource management in serverless computing environments presents unique challenges and opportunities due to the fundamentally different operational model compared to traditional server-based architectures. Serverless computing abstracts the underlying infrastructure from developers, allowing them to focus on writing code rather than managing servers. This abstraction, however, introduces complexities in resource management, necessitating innovative approaches to optimize performance, cost, and scalability. In serverless environments, resources such as CPU, memory, and storage are dynamically allocated and deallocated by the cloud provider based on the workload. This dynamic nature can lead to unpredictable performance characteristics and cost implications, as the resource usage scales with the number of function invocations. Whenever a function is invoked it must be served by a virtual machine. The major objective behind the function execution is to schedule the incoming function to the most suitable VM so that the VM resources can be utilized efficiently. It offers real-time scheduling of function instances that need to be executed to obtain the best performance and significantly lowers energy consumption. The study provides an extensive overview of resource management strategies in a serverless setting within this framework. Regarding cloud computing, a conceptual framework for workload prediction and resource management, classification of current machine learning-based resource allocation techniques, and key issues with inefficient physical resource distribution are covered. After that, a thorough analysis of current methods supporting machine learning-based methods in serverless resource management is provided. Ultimately, the study examines and summarises several recently identified issues as well as potential future research areas related to elastic resource management in serverless computing environments.