Multi-Party Computation (MPC) toolkits are designed to enable multiple parties to jointly compute results over private data without exposing the underlying sensitive information to one another. These frameworks are increasingly used in secure analytics, privacy-preserving machine learning, cryptographic key management, blockchain systems, and regulated industries such as finance and healthcare. Leading MPC toolkits such as MP-SPDZ, SCALE-MAMBA, EMP Toolkit, ABY, PySyft, FATE, and Sharemind differ in areas like protocol support, cryptographic security, scalability, ease of integration, and enterprise readiness. This discussion focuses on comparing these MPC solutions based on their real-world performance, developer usability, privacy guarantees, and suitability for research, production systems, and large-scale secure computation use cases.