Deep/ultra-deep oil and gas resources are abundant at vertical depths of more than 3,500 m, which is an important succeeding field for future oil and gas exploitation. However, a lack of understanding of the multi-scale mechanical behavior of deep reservoirs under in situ conditions, as well as an insufficiently accurate prediction of engineering sweet spots, restricts the effectiveness of hydraulic fracturing in deep shale gas exploitation. In this study, the application of cross-scale rock mechanics, digital rock core modeling, and machine learning in characterizing reservoir geomechanical properties and assessing engineering sweet spots was summarized. The challenges and future development directions of the above research elements were explored. To achieve efficient deep-resource exploitation, it’s essential to clarify the mechanical behavior of shales with different mineral compositions at micro- and macro-scales. Numerical models incorporating mineral spatial heterogeneity were developed to analyze the multifactorial synergistic mechanism influencing shale brittle failure. Finally, intelligent fracability prediction methods for deep shale were proposed to accurately identify engineering sweet spots. The research findings have identified the key research and development directions for deep-resources development from a rock mechanics perspective.