The field of materials science is undergoing a transformation driven by the integration of advanced computational methodologies. This chapter explores the application of machine learningMachine Learning (ML) toolsTools and web servicesWeb services in materials science modelingModeling, highlighting the significant advancements and efficiencies they bring to the discipline. Researchers can develop and deploy sophisticated models to predict and analyze material properties and behaviors by utilizing programming languages such as R, PythonPython, and C. Machine learningMachine Learning (ML) provides a robust framework for handling complex datasets, enabling the discovery of patterns and relationships that are not easily discernible through traditional methods. This chapter delves into various machine learning techniques, including regression, classification, clustering, dimensionality reduction, and their specific applications in materials science. It also discusses the pivotal role of web servicesWeb services in enhancing the accessibility and scalability of these computational toolsTools, fostering collaboration and resource sharing among researchers globally. The chapter offers an in-depth examination of the tools and libraries available in RR and PythonPython, such as Scikit-learn, TensorFlow, and the RR caret packagePackage, illustrating their use through practical examples and case studies. Additionally, it addresses the performance and efficiency benefits CC provides in developing computationally intensive simulations. Through detailed discussions and illustrative examples, readers will gain a comprehensive understanding of how to leverage these technologies to address complex materials science challenges.