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Several studies describe various surgical approaches, along with their combinations and modifications, to reach lesions of this region. Some of these surgical approaches have been compared using artificial intelligence-based approaches that can be predicted, classified, and analyzed for complex data. Several surgical approaches, such as anterior transsylvian, trans-superior temporal sulcus, trans-middle temporal gyrus, subtemporal\u2013transparahippocampal, presigmoid-retrolabyrinthine, supratentorial-infraoccipital, and paramedian supracerebellar-transtentorial, were selected for comparison. Magnetic resonance images (MRIs) were taken according to the criteria specified by the Radiology Department. With an open-source software tool, volumetric data from cranial MRIs were segmented and anatomical structures in the main regions were reconstructed. The Q-learning algorithm was used to find pathways similar to these standard surgical pathways. The Q-learning scores among the selected pathways are as follows: anterior transsylvian (Q_A)\u2009=\u200931.01, trans-superior temporal sulcus (Q_B)\u2009=\u200925.00, trans-middle temporal gyrus (Q_C)\u2009=\u200928.92, subtemporal-transparahippocampal (Q_D)\u2009=\u200923.51, presigmoid- retrolabyrinthine (Q_E)\u2009=\u200927.54, supratentorial-infraoccipital (Q_F)\u2009=\u200927.2, and paramedian supracerebellar-transtentorial (Q _G)\u2009=\u200921.04. The Q-value score for the supracerebellar transtentorial approach was the highest among the examined approaches and therefore optimal. A difference was also found between the total risk score of all points with pathways drawn by clinicians and the total risk scores of the pathways formed and followed by Q-learning. Artificial intelligence-based approaches may significantly contribute to the success of the surgical approaches examined. Furthermore, artificial intelligence can contribute to clinical outcomes in both preoperative surgical planning and intraoperative technical equipment-assisted neurosurgery. 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