The PLoM (Probabilistic Learning on Manifolds) software is a MATLAB-based toolbox designed to perform advanced probabilistic modeling and learning on manifolds. It is particularly useful for analyzing high-dimensional data from small datasets, reducing dimensionality, and capturing complex relationships in data through a probabilistic approach. The toolbox provides various types of analyses that facilitate the construction of probabilistic models on manifolds and statistical surrogate models, making it a powerful tool for researchers and practitioners in fields such as scientific machine learning, data science, and applied mathematics. The development of the PLoM software is grounded in the research published in the articles listed at the end of this file. Numerous applications of this software can also be found in the published literature. The software's capabilities are demonstrated through 9 Benchmarks. Each Benchmark illustrates a specific type of analysis that can be performed, with fully defined data sets and the associated results generated by PLoM. A Python version: "PLoM_Python_V1_2024", of the Matlab package: "PLoM_Matlab_V1_2024" is available on GitHub.