This survey study investigates the junction of Machine Learning (ML), networking, and the Internet of Things (IoT) within the framework of Low-Latency, Low-Energy Adaptive Clustering Hierarchy (LL-LEACH) protocols for Underwater Acoustic Networks (UANs). The deployment of UANs has special difficulties because of the hostile underwater environment, limited energy resources, and need of real-time data transfer. LL-LEACH systems optimize energy economy, reduce latency, and improve network performance by means of adaptive clustering techniques enhanced by machine learning algorithms to address these challenges. Emphasizing their fundamental features, advantages, and limitations, this work explores and assesses many recently proposed LL-LEACH methods. This study addresses decreasing latency and possible computer-assisted solutions for LEACH protocols in underwater acoustic networks. It also addresses how machine learning techniques may enhance network management, data processing, and decision-making capabilities as well as how IoT devices might be added into UANs. By means of this extensive analysis, we want to provide insights on the state-of- the-art LL-LEACH systems in UANs and suggest future research routes at the intersection of machine learning, networking, and IoT for underwater communication systems.