Rapid urbanization and increasing demand for sustainable transport have highlighted the critical need to strengthen last-mile connectivity within public transit systems. In India, e-rickshaws have emerged as a promising feeder mode owing to their cost-effectiveness, energy efficiency, and adaptability to diverse traffic conditions. However, optimizing their integration into existing transit systems remains challenging, particularly in terms of route planning and frequency setting. This systematic review investigates the current state of research on feeder system optimization, with a specific focus on the use of metaheuristic and fuzzy logic-based methods for enhancing e-rickshaw feeder networks. A total of 71 studies were selected through a comprehensive literature review spanning databases such as Google Scholar, Scopus, IEEE Xplore, and Web of Science. The selected studies were categorized thematically and analyzed for trends in methodology, geographical focus, and application. This review highlights the growing adoption of algorithms such as the genetic algorithm (GA), ant colony optimization (ACO), and hybrid approaches combining fuzzy logic to address multi-objective optimization challenges in feeder network design. The findings emphasize that hybrid methods particularly the integration of GA and fuzzy logic can effectively manage real-world uncertainties while optimizing service coverage and operational efficiency. Moreover, the socioeconomic and environmental advantages of e-rickshaws make them an ideal feeder solution for urban India. This study contributes a holistic perspective to feeder system planning, supporting the development of intelligent, inclusive, and sustainable multimodal transportation frameworks in rapidly growing cities.