With the increasing amount of generated data, organizations are turning to advanced analytics, data science, machine learning, and AI to drive better forecasting, more accurate predictions, and innovative solutions. However, many businesses fail to reap these benefits due to a lack of clear goals, poorly fitted teams, or insufficient access to high-quality data. To overcome these challenges, powerful graph technology can help by providing comprehensive domain knowledge, improved analytics, and new sources for machine learning feature engineering. Predictive modeling is the process of analyzing patterns in data to predict future outcomes, and its benefits include incorporating external variables, reducing time and cost, and enabling more informed business decisions. Common predictive tasks include regression, classification, anomaly detection, clustering, and similarity matching, each with various algorithms such as linear regression, logistic regression, decision trees, boosting methods, neural networks, KNN, and K-Means. Despite its potential, predictive modeling faces challenges like choosing the best model or algorithm, finding hidden insights for better decisions, and ensuring data security and privacy. Graph data science engines like Neo4j can help overcome these obstacles by providing a seamless integration with existing data ecosystems and enabling data scientists to make better predictions, build proof-of-concept models, and go to production quickly.