Abstract: The study addresses the crucial topic of designing and implementing smart systems in agricultural production, focusing on the development of a "Smart Greenhouse" utilizing neural networks. It thoroughly examines key technological innovations and their role in sustainable agriculture, emphasizing the collection, processing, and analysis of data to enhance plant growth conditions. The research highlights the efficiency of resource use, management of humidity, temperature, carbon dioxide levels, and lighting, as well as the automation of irrigation and fertilization. Special attention is given to developing adaptive algorithms for predicting optimal conditions that increase crop yield and quality while reducing environmental impact and costs. This opens new avenues for the sustainable development of the agricultural sector, promoting more efficient and environmentally friendly farming practices. Utilizing a literature review, comparative analysis of existing solutions, and neural network simulations for predicting optimal growing conditions, the study makes a significant contribution to applying artificial intelligence for greenhouse microclimate management. It explores the potential of AI in predicting and optimizing growing conditions, potentially leading to revolutionary changes in agriculture. The research identifies scientific innovations, including the development and testing of predictive algorithms that adapt to changing external conditions, maximizing productivity with minimal resource expenditure. The findings emphasize the importance of further studying and implementing smart systems in agriculture, highlighting their potential to increase yield and improve product quality while reducing environmental impact. In conclusion, the article assesses the prospects of neural networks in the agricultural sector and explores possible directions for the further development of "Smart Greenhouses".