To improve the performance of the power cycle and accelerate the realization of the carbon neutrality goal, the development and application of nanofluids are imminent. In this research, we established a supercritical nanofluid spectral absorbance prototype and prepared seven supercritical carbon dioxide nanofluids for various valuable works. The experiments indicated that titanium carbide/supercritical carbon dioxide had the most outstanding stability with the shortest stabilization time (3.2 min) and the smallest fluctuation amplitude in spectral irradiance (±6.33%). Graphene/supercritical carbon dioxide emerged as the most competitive choice in terms of deposition characteristics and spectral absorption capacity, boasting a deposition density and spectral absorbance of 8.146% and 86.39%. Titanium was the most suitable raw material for constructing the prototype. Then, a performance factor was introduced to quantify the contribution of stability and deposition characteristics, and hydroxylated multi-walled carbon nanotube/supercritical carbon dioxide was found to have the most outstanding comprehensive performance with promising applications. Finally, we combined particle swarm optimization and artificial neural networks to develop an improved machine learning optimization model. Compared to the original prediction model, the root mean square error and mean absolute error of this model were reduced by 64.02% and 66.8%. Significantly, the spectral irradiance through the supercritical carbon dioxide nanofluid was reduced by 68.21% compared to the base working fluid, providing guidelines for improving the performance of the power cycle.