Accurate measurement of aerosol optical properties is critical for understanding their radiative and environmental impacts. Currently, the most accurate retrieval of aerosol properties comes from the multi-channel surface sun photometer, but with relatively high cost and deployment/maintenance requirements. Here we develop a novel AI based method for retrieving daytime aerosol optical parameters, namely aerosol optical depth (AOD) and single scattering albedo (SSA) using images acquired by All-Sky Imagers (ASI). Surface based AOD and SSA retrievals from surface sun photometers are used as the training targets. Algorithm training and retrievals were performed for two sites in East China and Central US respectively. Independent validation against ground-based measurements demonstrated high consistency between the ASI-retrieved and sun photometer measured AOD and SSA, with Pearson correlation coefficients (r) exceeding 0.86 for AOD across all wavelengths at both sites and Root Mean Square Errors (RMSE) below 0.25. For SSA, r values reached 0.67 at the Beijing_PKU site and 0.84 at the SGP site, with RMSE remaining below 0.09 across all spectral channels, demonstrating the feasibility of simultaneous AOD and SSA retrieval from low-cost all-sky imagers. This method not only overcomes the high computational cost associated with traditional radiative transfer iterative algorithms, but also provides great potential for denser surface aerosol measurements by leveraging the low-cost and easy-maintenance advantages of the all-sky imager.