Currently, there is a scarcity of studies that comprehensively address all structural components in the building's overall environmental performance. Although mathematical optimization has shown its great effectiveness, the prevailing iterative design approach continues hindering the simultaneous optimization of building structural performance and its sustainability. This research aims to address this gap by introducing an innovative optimization framework that integrates optimization concepts into the design process using Artificial Neural Networks (ANNs). The proposed framework includes a carbon-optimization problem for flat slab buildings, which is validated with manual designs, and a surrogate modelling stage employing ANNs to predict optimal design solutions. The best network demonstrates remarkable predictive power, yielding minimal errors and high fitness (>0.96) for all variables, except slab thickness. Sensitivity analysis identifies the building's height and dimensions as the most influential factors on the slab reinforcement, column reinforcement, and carbon footprint. While span length and slab concrete strength greatly impact slab and drop depths, column size is primarily controlled by span length and building's height. This research not only provides crucial insights into sustainable design solutions but also paves a clear pathway towards achieving decarbonization and cleaner production across the entire building sector.