In advanced medical treatments, hip replacement is widespread for treating joint damage. However, correct implant identification in revision surgeries becomes challenging due to integrated uncertainties. This article aims to introduce an intelligent approach based on fuzzy pattern recognition to reduce uncertainty and ambiguity in implant identification during revision surgeries focusing on hip replacement. A well-known fuzzy framework, picture fuzzy rough set (PFRS), is utilized to introduce a new pattern recognition algorithm. In this article, the new similarity measures (SMs) are introduced for PFRS. Some fundamental properties of the introduced SMs are investigated. Then, the proposed SMs are used to formalize the pattern recognition algorithm. Finally, the proposed algorithm is utilized to identify the most suitable implant for joint replacement. The developed intelligent model reduces uncertainty and ambiguity in collected data using the idea of approximations, which classifies the information into boundaries to seek the perfect information for exact implant identification. In addition, the developed approach is the generalized approach of pattern recognition integrating rough set (RS) with fuzzy logic, which leads to accuracy enhancement in identifying implant types. The developed approach helps streamline revision surgeries and improve patient outcomes by reducing surgical complexities.