Efficient toll management is key to reducing congestion and improving traffic flow on busy highways. This research study presents an intelligent and adaptive toll lane allocation framework tailored for the Neelamangala Toll Plaza on National Highway 48, India. The proposed system integrates Internet of Things (IoT) technologies, machine learning-driven predictive analytics, and real-time traffic monitoring to allocate toll lanes and enhance operational efficiency dynamically. IoT-enabled sensors, RFID tags, and surveillance cameras continuously capture real-time data on vehicle volume, traffic density, and queue lengths. These data streams are processed using advanced predictive models to anticipate congestion patterns and optimize lane distribution. The research methodology framework uses a strong mathematical model and real-time and historical data to optimize traffic flow and improve efficiency. A key innovation of this system is a driver-centric assistance mechanism, featuring a digital display board positioned one kilometer ahead of the toll plaza. This board provides real-time lane recommendations and estimated waiting times, facilitating informed decision-making for drivers. Additionally, an automated SMS notification system linked to RFID-enabled vehicles ensures seamless communication, improving user experience and transaction efficiency. Experimental results demonstrate a substantial reduction in average processing time, queue lengths, and vehicle idling, leading to increased throughput and improved environmental sustainability. The system has shown significant reductions in fuel consumption and emissions, aligning with global sustainability objectives. Furthermore, the scalability of the proposed framework makes it adaptable to other high traffic toll plazas and multi-lane highway systems, with potential integration into broader smart city infrastructure. This research contributes valuable insights into the application of IoT and machine learning for real-time traffic prediction and dynamic toll lane assignment, addressing key limitations of traditional static toll systems. The findings offer a scalable, future-ready solution for policymakers, urban planners, and transportation authorities seeking to modernize toll operations, enhance user experience, and improve overall road network efficiency.