Sustainable and efficient agricultural techniques are becoming essential to meet the growing food demand while preserving natural resources. AI-enhanced greenhouses in India use advanced technologies like sensors, automation, and data analytics to optimize crop growth conditions. These systems help regulate temperature, humidity, and irrigation, resulting in higher yields and reduced resource wastage. Additionally, they minimize the use of pesticides and fertilizers, promoting environmentally friendly farming. This case study highlights how integrating AI with greenhouse farming can improve productivity and sustainability in Indian agriculture. Control of greenhouse conditions. Existing control algorithms commonly rely on closed-loop feedback or simplified models that do not accurately capture the nonlinear, dynamic, and stochastic characteristics of greenhouse conditions. In contrast, the application of reinforcement learning algorithms allows for self-controlled problem-solving by continuously learning the control policy through involvement of the environment. Conceptual framework of AI-enabled greenhouses, greenhouse microclimate factors like temperature, humidity, CO² concentration, and illumination intensity, and the implications of reinforcement learning for the optimization of these factors with regards to enhanced plant growth, efficiency, and energy use, will be covered in this chapter. Challenges related to data availability, system safety, model interpretability, and real-world deployment are discussed, along with emerging solutions such as hybrid control architectures and digital twins. By highlighting recent advances and practical considerations. This chapter consist of case study related to Precision viticulture/ AI-Enhanced Greenhouse etc.