A methodology is demonstrated to estimate brittleness coefficient and in-situ stress from well log data linking post-stack seismic data in the unconventional Raghavapuram Shale reservoir of Krishna-Godavari basin, India. Post-stack seismic data has been used to estimate pore pressure, in-situ stress from well logs through multilayered feedforward neural network (MLFN) model. Shear impedance and density sections from post-stack seismic data are generated from conversion of acoustic impedance to shear impedance linking with well log data. Inverted acoustic impedance, shear impedance, density and compressional to shear wave velocity ratio (Vp/Vs) have been used to train the MLFN model for mapping pore pressure and vertical stress obtained from well logs. Minimum horizontal stress is computed for anisotropic shale medium. Differential stress ratio (DHSR) section is generated from maximum and minimum horizontal stress magnitudes using MLFN models. Brittleness coefficient estimated from static Young's modulus and Poisson's ratio is mapped into post-stack section. Fracture index is computed from dipole shear sonic log data. The brittleness coefficient of 20–45% and DHSR of 7–16% with 20–46% fracture index are noticed in the Raghavapuram Shale. This zones exhibit relatively high values of brittleness coefficient and relatively high DHSR. Fractures will be aligned in high DHSR zones and oriented to the direction of maximum horizontal stress. This information may be useful for fracture type prediction and designing hydraulic fracture stimulation.