Advancements in artificial intelligence (AI) and Internet of Things (IoT) technologies have revolutionized climate management in hydroponic agriculture by enabling real-time monitoring, predictive analytics, and adaptive environmental control. This chapter provides a comprehensive overview of how integrated sensor networks, machine learning (ML) algorithms, and cloud-edge computing architectures collectively transform greenhouse microclimate regulation. A detailed exploration of sensor technologies including thermocouples, quantum light sensors, and electrochemical probes demonstrates their role in capturing dynamic environmental parameters such as temperature, humidity, light intensity, and nutrient composition. The application of supervised learning, neural networks, and reinforcement learning techniques is critically examined in the context of predictive modeling and autonomous decision-making for optimized plant growth. Emphasis is placed on model validation metrics such as RMSE, R2, and MAPE, alongside real-world challenges like data variability, sensor drift, model interpretability, and sustainability concerns. Figures and flowcharts enhance understanding of ML pipelines, validation strategies, and system integration. The chapter concludes by addressing ethical implications, equity in access, and the importance of explainable AI (XAI) frameworks to facilitate trust and adoption in agricultural communities. Overall, this chapter serves as a foundational reference for researchers and practitioners aiming to implement intelligent, scalable, and sustainable climate control in modern hydroponic systems.