CheminformaticsCheminformatics has become indispensable due to the exponential growth of reported chemical data and the increasing demand for efficient data processing, management, and analysis techniques. This chapter offers a comprehensive introduction to cheminformatics, particularly on predictive modelingPredictive modelling. It begins by tracing the historical developments of the field, demonstrating how advancements in computational power and data availability have transformed cheminformaticsCheminformatics. The discussion includes key resources and methodologies for representing chemical datasets, which are crucial for conducting accurate and meaningful analyses. Several modeling strategies, such as regressionRegression, classificationClassification, and clusteringClustering, with a special focus on both traditional linear modelsLinear models and advanced machine learning and deep learning techniques will be also explored. Additionally, the importance of model validationModel validation and interpretation to ensure the reliability and applicability of predictive models in real-world scenarios will be covered and highlighted. By covering these topics, the chapter provides a thorough overview of the tools and approaches that are driving innovation in cheminformaticsCheminformatics, ultimately paving the way for breakthroughs in drug discovery, material design, and beyond.