Wireless Sensor Networks (WSNs) are an essential
technology for monitoring environmental and physical
conditions. However, energy constraints limit their
operational lifespan, making energy-efficient optimization
crucial. This paper explores the role of Artificial
Intelligence (AI) in addressing the challenges including
routing, data aggregation, and node management through
various AI techniques such as machine learning (ML),
deep learning (DL), and heuristic optimization. Machine
learning enables adaptive decision-making, deep learning
enhances energy prediction and network performance and
heuristic methods like Genetic Algorithms (GA) optimize
tasks like routing and scheduling. Hybrid AI approaches
further improve energy optimization by combining the
strengths of multiple methods.