**Abstract**

In the last few decades, wireless sensor network (WSN) emerged as an important network technology for real-time applications considering its size, cost-effectiveness and easily deployable ability. Under numerous situations, WSN may change dynamically, and therefore, it requires a depreciating dispensable redesign of the network. Machine learning (ML) algorithms can manage the dynamic nature of WSNs better than traditionally programmed WSNs. ML is the process of self-learning from the experiences and acts without human intervention or re-program. The current Chapter will cover various ML Algorithms for WSN and their pros and cons. The reasons for the selection of particular ML techniques to address an issue in WSNs, and also discuss several open issues related to 'ML for WSN'.

**Keywords:** wireless sensor network, machine learning, supervised learning, unsupervised learning, reinforcement learning
