Hybrid Renewable Energy Systems (HRES) are becoming a viable method to provide the ever-increasing energy demands with less reliance on traditional fossil-fuel power generation. Sizing of the components of these systems is a critical design element that has a significant impact on the performance, reliability and economic viability of the systems. The present paper provides a detailed review of the prominent sizing methods used in HRES, such as traditional optimization methods, AI-based methods, hybrid optimization methods, and software-assisted design methods. The study reviews the literature and commonly adopted modeling platforms in depth and analyzes the operational principles, advantages, disadvantages and suitability of the methods for solving the highly non-linear, uncertain and multiobjective nature of HRES design problems. Intelligent and hybrid optimization methods are usually superior when dealing with complex search spaces, uncertainty and conflicting design goals, software-based tools are usually more practical and user friendly when analyzing systems, given some modelling limitations. The results also reveal that there is no universally best sizing technique, as it is dependent upon system requirements, resources characteristics, optimization goals and user expertise. The study offers a new comparative review of the existing sizing approaches, which can serve as useful guidance for energy planners, engineers and researchers aiming to increase system reliability, reduce lifecycle costs and pursue the deployment of sustainable energy in remote and underserved regions. Further, the review highlights new research areas and gaps that can be used to augment the creation of more comprehensive and efficient HRES sizing frameworks.