<p>Precise heating load prediction would be fundamental in optimizing energy consumption and assuring efficient operation of heating systems. Advanced machine learning methods were explicitly employed in this respect, using a random forest regressor that has shown high precision in heating load prediction. Then, the developed model was optimized by using two different methodologies: Sea Horse Optimizer and Equilibrium Slime Mould Algorithm. The approach of optimization ensembles, when put together, is termed RFSE, which brings out unique strengths and further enhances the overall accuracy of predictions. Of special note was the synergy between the RF model and the SHO, the interaction of which had the RFSH model showcase extraordinary predictive prowess. The RFSH model had a very impressive R-squared of 0.997, showing that this model fits the data very well, and the RMSE value was as low as 0.550, hence showing minimal error in predictions. This, therefore, makes the RFSH model very reliable while estimating the heating load. In this work, the strategic integration of ML with specialized optimization techniques underpins immense potential in solving complex real-world challenges, such as heating load prediction. This paper, therefore, constitutes a very important milestone in the quest toward optimization of energy efficiency and heating systems: precise and effective heating load forecasting. These advances ultimately contribute to energy conservation and cost reduction in heating applications.</p> Graphical abstract <p></p>