This dissertation proposes novel modeling and control methods for thermal and energy management applications, specifically focusing on Implantable Medical Devices (IMDs) and Heating, Ventilation, and Air Conditioning (HVAC) systems. Both applications consider energy consumption with additional constraints: IMDs aim to maximize power consumption while avoiding tissue damage from overheating, whereas HVAC applications focus on saving energy while maintaining occupants' thermal comfort. This thesis proposes a novel online prediction algorithm to model thermal dynamics of IMDs with multiple heat sources. Additionally, a Model Predictive Control (MPC) scheme is proposed to achieve real-time thermal and power management of neural IMDs. Furthermore, the dissertation develops a feature importance analysis method utilizing meta-learning to evaluate importance of features used in modeling thermal comfort of occupants in HVAC applications.