A significant step in the design of heating, ventilating, air conditioning, and refrigeration (HVAC-R) systems is to calculate room thermal loads. The heating/cooling loads encountered by the room often vary dynamically while the common practice in HVAC-R engineering is to calculate the loads for peak conditions and then select the refrigeration system accordingly. In this study, a self-adjusting method is proposed for real-time calculation of thermal loads. The method is based on the heat balance method (HBM) and a data-driven approach is followed. Live temperature measurements and a gradient descent optimization technique are incorporated in the model to adjust the calculations for higher accuracy. Using experimental results, it is shown that the proposed method can estimate the thermal loads with higher accuracy compared to using sheer physical properties of the room in the heat balance calculations, as is often done in design processes. Using the adjusted real-time load estimations in new and existing applications, the system performance can be optimized to provide thermal comfort while consuming less overall energy.