It is hard to get smart buildings to operate on net-zero energy mainly because energy consumption, occupant comfort, and renewable energy integration are nonlinear, multi-objective, and dynamic. Traditional optimization and rule-based control methods do not solve the problem effectively as they are less adaptive to changing environmental conditions and complex system interactions. As a result, they lead to suboptimal performance and increased reliance on the grid. In that regard, the present paper introduces an innovative hybrid metaheuristic optimization tool that combines the Centered Collision Optimizer (CCO) and Spider Wasp Optimization (SWO) for adaptive and real-time energy management. This new tool uses a variance-driven switching method to constantly adjust the balance between global exploration and local exploitation, thus preventing early convergence and improving the quality of the solution. Among other methods, the proposed CCO-SWO model has been shown to significantly outperform standard methods such as PSO, GWO, and HHO in EnergyPlus and MATLAB simulations. The report reveals a 31.5% drop in total energy consumption, a 41.3% less reliance on the grid, and an enhanced level of thermal comfort with a deviation of only 0.94C, whereas the high Net-Zero Index (NZI) of 0.96 was also maintained. These results prove the proposed framework as a highly efficient, reliable, and versatile approach for intelligent energy controlling in smart buildings strongly supporting sustainable and net-zero energy system deployment.