Hybrid energy systems, integrating diverse energy sources such as solar, wind, and storage battery, are essential for granting reliable and sustainable power to remote and isolated areas. The design and sizing of these systems are complex tasks that need careful consideration of various criteria, including energy demands, resource availability, and system efficiency. This review paper systematically evaluates and compares different design and sizing methods for off-grid hybrid energy systems. We explore both conventional approaches, such as deterministic and probabilistic methods, and advanced techniques, including optimization algorithms and simulation-based models. Conventional methods often rely on historical data and simplified models, while advanced techniques, such as genetic algorithms, particle swarm optimization, and hybrid approaches, offer more precise and adaptable solutions by handling complex, multidimensional problems. Through a comprehensive analysis, this review highlights the strengths and limitations of each method, providing insights into their effectiveness in different scenarios. The findings aim to guide researchers and practitioners in selecting appropriate methodologies for optimizing the design and performance of off-grid hybrid energy systems. By identifying best practices and recommending strategies, this review contributes to the advancement of efficient and sustainable energy solutions for off-grid applications. This review could be a base study for researchers who aim to optimize a renewable energy-based hybrid system.