In this chapter we have presented in an understandable way a series of applications of fast matrix multiplication algorithms in linear algebra problems and tackled some non-matrix problems. Each algorithm is demonstrated using a concrete example of decent dimensionality to ensure that all the mechanisms of the algorithms are revealed. We have surveyed 34 other applications of fast matrix multiplication algorithms, for example in AI (neural networks), linear optimisation, optimal route finding, sparse matrices, databases (query processing), string matching, computational biology, pattern recognition (object detection and classification), compiler construction (optimal memory management, formal grammars), special matrices (e.g. Toeplitz type), parallel computing, and others. Importantly, all the algorithms presented can also use Intel's Advanced Matrix Extensions (AMX) commands, Nvidia CUDA, and any other hardware matrix multiplication accelerator as the core matrix multiplication algorithm.