The characterization of strength and elastic properties of shales is a critical step in determining the hydrocarbon resource and extraction potential of a formation. Their inherent heterogeneity and bedding anisotropy make shales mechanically complex. Properties like mineral composition, organic content, pore structure, and thermal maturity have profound impacts on shale’s mechanical behavior. Laboratory techniques like compression test, Brazilian test, and fracture toughness test are employed to measure the static mechanical properties of shales. However, these techniques require intact shale rock specimens of standard sizes which is not always available from cores recovered from wellbores due to the fissile nature of shale. Thus, micromechanical properties of shales are evaluated at finer scales using nanoindentation and atomic force microscopy techniques. These techniques reveal the mechanical heterogeneity between the organic phase and mineral phase in the shale matrix. The behavior of shales under in-situ stress and temperature conditions can be modelled using numerical simulations. Upscaling models also help in predicting the bulk-rock mechanical behavior of shales from their measured nano- and micro-scale mechanical properties. Recent advancements in machine learning techniques have enabled the use of well-log data to predict mechanical properties for large-scale reservoir assessments when core data is limited. Integration of aforementioned methods allow for efficient reservoir modelling, improved designs of wellbore fracture-stimulation programs, and safer drilling methods.