Effort estimation is a crucial feature for every software development team. Overestimation and underestimation both can be harmful for the developer. Overestimation can cause resource waste or perhaps project rejection and underestimation can lead to budget overruns or delivery delays or staffing shortage. A Big Challenge comes in front of developers to deliver the project on time and within budget. Effort estimation is an important aspect of software development projects, as it plays a vital role in project planning, resource allocation, and cost estimation. Accurate effort estimation helps in managing project timelines, optimizing resource utilization, and delivering high-quality software within budget constraints. However, traditional estimation methods often rely on subjective judgments and expert opinions, which can result in inconsistent and unreliable estimates. So, the objective of this paper is to identify the machine learning techniques, various datasets and measuring metrices from the literature which can be used further for the estimation.