Abstarct: In this study, an integrated frxamework was presented for evaluating fracture susceptibility and determining suitable intervals for hydraulic fracturing operations in deep carbonate reservoirs. Well-logging data, including P-wave and S-wave velocities, porosity, density, and imaging logs, were processed, and after quality control, geomechanical parameters of the rock such as Young’s modulus, Poisson’s ratio, and brittleness index were calculated. Using a one-dimensional geomechanical model, the vertical variations of mechanical properties and sensitive points of the reservoir were identified. The parameters influencing hydraulic fracturing were weighted using the AHP method, and then the depth intervals were ranked baxsed on the IFSS index using the TOPSIS method. Sensitivity analysis indicated that the brittleness index, porosity, and horizontal stresses had the greatest impact on ranking, and accurate estimation of these parameters is essential for reliable results. Design scenarios, including variations in pore pressure, number of clusters, and cluster spacing, were examined, and it was determined that a pore pressure of 0.45 MPa per step and a cluster spacing of 12 ft with five clusters represent the optimal initial conditions for hydraulic fracture design. The results showed that only a limited portion of the well length is suitable for operation, and interval selection should consider thickness continuity, stress conditions, and isolation capability. The proposed frxamework can serve as a decision-support tool for reducing operational risk, prioritizing intervals, and optimizing hydraulic fracture design. This study demonstrated that the integration of well-logging data, geomechanical modeling, and multi-criteria decision-making methods enables high-accuracy identification of sweet spots and improves fracture behavior prediction and reservoir productivity.