Public transportation is evolving from fixed-route, static schedules to dynamic, demand-responsive systems that leverage real-time data and AI. Modular vehicles with attachable compartments can adjust capacity according to passenger flow, enabling flexible service. Dynamic routing algorithms optimize travel paths to avoid congestion and minimize delays. These systems improve reliability and user satisfaction while reducing operational costs and environmental impact. Challenges include infrastructure adaptation, safety protocols, and gaining public trust. Successful implementation promises a resilient, efficient public transit network that responds fluidly to urban mobility needs.