Recent research in Congestion Control Techniques in Mobile Ad Hoc Networks (MANETs) focuses on mitigating packet loss, delay, and energy wastage caused by network congestion in highly dynamic environments. Traditional TCP-based schemes are being enhanced through machine learning-driven congestion prediction, neuro-fuzzy algorithms, and adaptive routing mechanisms that adjust traffic flow based on real-time network conditions. Protocols like Congestion-Adaptive Routing (CRP) and rate-aware hybrid models leverage node density, queue length, and mobility metrics to detect and prevent congestion proactively. Active Queue Management (AQM) techniques such as RED and Drop Tail are also being integrated with routing protocols like AODV to maintain QoS and fairness. Recent advancements emphasize cross-layer optimization, intelligent rate control, and energy-aware traffic scheduling, ensuring efficient resource utilization and improved end-to-end performance in MANETs operating under high mobility and limited bandwidth conditions.