Traffic congestion remains a significant challenge in urban mobility, necessitating innovative and cost-effective monitoring solutions. This research explores the development of intelligent magnetic sensing systems as an alternative to traditional camera-based and GPS-dependent traffic monitoring technologies. Magnetic sensors provide a low-cost, non-intrusive approach to vehicle detection, classification, and congestion analysis, leveraging perturbations in the Earth's magnetic field caused by moving vehicles. The study employs a structured methodology, integrating IoT-based real-time data processing, machine learning models for vehicle classification, and edge computing for congestion prediction. Data analysis results indicate an average vehicle classification accuracy of 89%, with buses achieving the highest detection rate (92%) and motorbikes presenting the lowest (85%) due to smaller magnetic signatures. The study also highlights key research gaps, including limited deployment in urban environments, challenges in differentiating similar vehicle types, and scalability issues. Based on these findings, several recommendations are proposed, such as adaptive sensor placement, multi-sensor fusion, AI-driven classification enhancements, and integration into smart city infrastructures. Future research should focus on developing standardized communication protocols, refining predictive analytics, and ensuring seamless interaction with Vehicle-to-Infrastructure (V2I) networks. The results suggest that intelligent magnetic sensing systems can play a pivotal role in next-generation urban mobility solutions, offering real-time, privacy-friendly, and scalable traffic monitoring capabilities for intelligent transportation systems (ITS).