This study presents the most extensive and temporally grounded review of the transit network design problem (TNDP) to date, covering five decades of research and offering a unified perspective on its two core subcomponents: the transit route network design problem (TRNDP) and the frequency setting problem (FSP). As cities face mounting pressures from urbanization, climate change, and equity demands, the strategic design of public transit networks has become increasingly critical. Despite the problem’s centrality to transportation planning, the field remains fragmented and methodologically saturated, lacking integrated approaches that reflect real-world complexity. This review addresses that gap by analyzing over 170 studies from 1970 to 2024, systematically categorizing them by methodology, objective function, network scale, and system application. It is the first study to employ decade-resolved visual analytics, including heatmaps and taxonomies, to illustrate methodological trends, such as the rise of metaheuristics in the 2010s, the emerging—but still limited—role of AI/ML post-2020, and the declining prominence of classical optimization models. The study also introduces a novel scalability–performance matrix, comparing 10 solution approaches across multiple dimensions, and highlights the integration of TRNDP and FSP as a pivotal frontier in transit research. In doing so, it reveals critical research gaps, particularly the lack of resilience, equity, and adaptability in existing models, and proposes a forward-looking agenda rooted in unified, real-time, and data-driven frameworks. The review offers both a historical map and a strategic roadmap for scholars and practitioners seeking to advance sustainable and inclusive urban transit systems. The scientific value of this work lies in its combination of historical depth and methodological synthesis, introducing a novel scalability–performance matrix, decade-resolved visual analytics, and an integration-focused framework that has not been attempted in prior reviews.