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Machine learning-based side-channel attacks (MLSCAs) have demonstrated the capability to extract secret keys from AES by learning the correlation between leakages from power traces or timing of AES execution. Previous work has focused on unmasked AES, the captured power traces for profiling and testing have been collected from the same device, and they are primarily implemented on microcontrollers. In this paper, we present a comprehensive MLSCA that considers both masked and unmasked AES running on software and hardware with a side-channel leakage model under four scenarios involving two target boards (Artix-7 XC7AI00T FPGAs and STM32F415 microcontrollers) and different keys for training and testing the model. Our implementation results indicate that support vector machines outperformed other machine learning techniques on masked software and unmasked software AES with only 4 traces. Long short-term memory networks were found to outperform other techniques on unmasked hardware AES (FPGA) with only 283 power traces.