Urban transportation systems are facing unprecedented pressure due to rapid urbanization, infrastructure strain, and persistent mobility inequalities. As congestion, environmental degradation, and service unreliability intensify, there is a growing need for transformative approaches that go beyond incremental reforms. This study explores the role of Artificial Intelligence (AI) as a catalyst for modernizing transport networks by enhancing operational efficiency, advancing transport equity, and supporting climate resilience. Utilizing a systematic review grounded in the PRISMA framework, the research synthesizes insights from 43 peer-reviewed sources and policy reports spanning 2018–2025, with comparative analysis of three diverse urban contexts: Singapore, Shanghai, and Lagos. The study investigates AI applications in traffic optimization, public transit planning, freight logistics, and predictive infrastructure maintenance. Findings reveal measurable gains, such as a 30% reduction in congestion and up to 20% improvement in logistics efficiency. However, these outcomes vary widely depending on digital infrastructure, institutional capacity, and local data availability. Importantly, the analysis highlights underexplored dimensions of AI implementation, including gender-based mobility disparities, affordability constraints, and the risk of algorithmic bias in underserved regions. The research concludes that AI holds immense potential as a tool for enabling more inclusive, adaptive, and environmentally sustainable transport systems. Yet its successful deployment requires ethical governance, phased investment strategies, gender-responsive design, and stakeholder engagement, especially in emerging cities where resource constraints are significant. The paper offers actionable policy recommendations tailored to diverse urban settings, positioning AI not as a luxury technology but as a strategic lever for equitable urban mobility transformation.