The primary objective of this study is to enhance the accuracy of adult height prediction using parental height as a key factor. The methodology includes comprehensive data preprocessing steps such as eliminating duplicate records, encoding gender numerically, and computing Mid-Parental Height (MPH) as a crucial feature. Data visualization techniques were employed to analyze the correlation between parental and child heights, with separate comparisons for sons and daughters. The study explores multiple machine learning models, with a special focus on Support Vector Regression (SVR) due to its robustness in capturing nonlinear relationships. Other models, including Linear Regression, XGBoost, LightGBM, and Artificial Neural Networks (ANN), were also evaluated on both raw and cleaned datasets. A detailed analysis highlights the impact of outlier removal, demonstrating a significant improvement in model performance. Among the evaluated models, SVR emerged as the most accurate, particularly after outlier removal, underscoring the importance of preprocessing techniques in height prediction. This research provides valuable insights into optimizing predictive models and emphasizes the potential of machine learning in aiding pediatricians, researchers, and parents in tracking growth patterns and making informed decisions.