The greenhouse industry has received great attention and experienced tremendous growth in the recent past across the globe. However, energy consumption and labor cost in greenhouses account for more than 50 of the cost of greenhouse production. This demands an Internet of Things (IoT)-based smart solution for automation of greenhouse environment-related activities to ensure maintenance of the desired climate inside the greenhouse to maximize plant production with optimal resource utilization. To this end, several models are proposed in the literature that is based on a selected artificial intelligence (AI) algorithm which is once trained and then deployed. The drawback of such systems is that the trained models are fixed (locked) and, therefore, unable to adapt to dynamically changing conditions, which results in performance degradation. Second, the existing studies on the subject matter are focused on the individual key component (i.e., prediction, optimization, and control). In this article, a novel scheme is presented based on the integration of the key components, and the performance of prediction and optimization components is further enhanced through the exploitation of artificial neural network (ANN)-based learning modules to support autonomous greenhouse environment monitoring and control. For experimental analysis, the greenhouse environment is emulated through the mathematical formulation of essential greenhouse processes, considering the impact of actuators' operations and external weather conditions. Real environmental data collected for Jeju Island, South Korea is used for model validation and result analysis. Proposed learning-based optimization scheme results are compared with two other schemes, i.e., baseline scheme and optimization scheme. Comparative analysis of the results shows that the proposed model maintains the desired indoor environment for maximizing plant production with reduced energy consumption, i.e., it achieves 61.97 reduced energy consumption than the baseline scheme, 11.73 better than the optimization scheme without learning modules. Furthermore, the proposed model achieves 67.96 and 12.56 reduction in cost when compared to the baseline scheme and optimization scheme without learning modules, respectively. © 2014 IEEE.