###### Abstract  
Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs. machine learning; routing algorithms; energy efficient; wireless sensor networks +
Footnote †: journal: Networks  
Traditional green routing schemes focus on clustering and selecting special nodes to control the data flow and extend the lifetime of WSNs [39; 40; 41]. The low-energy adaptive clustering hierarchy (LEACH) routing algorithm [42] divides sensor nodes into multiple clusters and utilizes high hierarchical cluster head nodes to uniformly receive and process the sensing data in the cluster. Hence, the member nodes in the cluster can avoid the energy consumption caused by forwarding. In [43], data forwarding was separated from data transmission by setting up an assistant node to avoid the premature death of some nodes due to frequent data forwarding. However, these methods have several drawbacks. Firstly, they depend on precise mathematical models that are very difficult to formulate and consume too much energy. Secondly, the suitability of those methods is not adaptive to varying network topology and different scales, which makes WSNs prone to congestion and thus impractical. Therefore, it is necessary to adopt new methods to alleviate these problems [44]. Machine learning (ML)-related techniques have recently helped to address the limitations of traditional green routing in WSNs [45; 46; 47], which provides a versatile and flexible paradigm when dealing with data and computation to solve complex problems that exactly match the requirements for the design of efficient routing algorithms in WSNs [48; 49; 50]. Despite the increasing interest in ML in the WSN domain, a comprehensive overview focusing on ML for green routing algorithms in WSNs is lacking. In order to promote the application of ML-based routing algorithms in WSNs, this paper introduces a complete overview of progressive research in this field. This paper aims to fill the gap between ML and routing algorithms in WSNs by offering the most advanced overview for interested practitioners to further promote the development of this field. Since it is difficult to recharge sensors after deployment, it is necessary to consider how to rationalize the limited energy of each node and extend the lifetime of WSNs. ML offers a generic and flexible paradigm for evaluating complex problems that perfectly match the requirement of energy-efficient routing in WSNs. Thus, the novel contributions of this paper are as follows:  
1. We present a complete overview of advanced research within the domain of green routing algorithms in WSNs, specifically focusing on the use of ML;
2.