Hydroponic Green Forage (FVH) represents a good sustainable alternative to the challenges of the agricultural sector, especially in contexts of water scarcity and soil damage. However, its efficient production requires precise management of environmental elements such as temperature, humidity, light and pH. This systematic review aims to analyze the available scientific evidence on the implementation of good smart technologies in forage production, in particular focusing on three research questions: (1) the use of Big Data for decision-making based on environmental data; (2) the application of invisible ambient intelligence using IoT and smart sensors; and (3) the impact of machine learning on the accurate detection of critical variables. In recent years, new technologies such as the Internet of Things (IoT), Big Data and Artificial Intelligence (AI) have been applied to agricultural systems to improve these processes with good automation, real-time monitoring and predictive analytics. The search was carried out in databases such as Scopus, ScienceDirect, ACM and IEEE Xplore, with inclusion criteria defined between 2015 and 2025. The results show that automation based on these technologies increases the efficiency of the use of resources (water and nutrients), improves forage quality, and reduces operating costs. This review provides an up-to-date and critical synthesis of technological advances applied to FVH, serving as a basis for future research and innovations in smart agriculture.