Supercritical CO2 (sCO2) stands out for concentrating solar power (CSP) due to its superior thermophysical and chemical properties, promising higher cycle efficiency compared to superheated or supercritical steam. Leveraging the waste heat from sCO2 cycles through the organic Rankine cycle (ORC) as a low-grade energy source enhances overall thermal efficiency. This research explores advanced sCO2 power cycles and introduces a novel approach by integrating machine learning and genetic algorithms for optimizing cycle performance. Utilizing a thermodynamic model-derived dataset, various machine learning algorithms, including Random Forest, XGBoost, and Artificial Neural Network are employed for prediction, evaluation and optimization. This innovative integration enables a comprehensive understanding of the complex dynamics of sCO2 power cycles. Subsequently, the study employs multi-objective optimization for the systematic evaluation and optimization of the combined power cycles, incorporating multiple bottoming cycles to maximize efficiency. The findings not only showcase the superiority of the unified sCO2ORC cycle but also emphasize the impact of integrating advanced computational methods in achieving optimal performance. The sCO2 cycle is explored in recompression, partial cooling, and main compression intercooling configurations. Recompression cycles utilize a single cooling system, while partial cooling and main compression intercooling layouts integrate a pair of ORCs at two precoolers. The ORC cycle enhances the recompression cycle through heat recuperation, extracting enhanced power originating from the bottom cycle. Critical parameters are taken into consideration to carry out optimization and performance evaluations. such as cycle temperatures, recuperator effectiveness, bottoming cycle pressure ratio, and condensation temperature. Results indicate that the combined partial cooling sCO2ORC cycle yields the optimal net output power of 7994.541 kW and thermal efficiency of 53.365 %. The findings highlight the capacity to propel and promote the utilization of waste heat recovery in energy technologies through enhanced and optimized power cycle designs, contributing to their advancement and widespread adoption.