This study explores the application of machine learning for enhanced abdominal fat prediction after cavitation treatment. The research investigates the potential of cavitation in non-invasive abdominal fat reduction and body contouring, focusing on optimizing fat prediction models using advanced hyperparameter optimization techniques, Hyperopt and Optuna. The objective is to improve the predictive accuracy of abdominal fat dynamics post-cavitation treatment. A robust dataset with abdominal fat measurements and cavitation treatment parameters is used to evaluate the efficacy of the approach through regression analysis. The performance of Hyperopt and Optuna regression models is assessed using metrics such as mean squared error, mean absolute error, and R-squared score. Results show that both models exhibit strong predictive capabilities, with R-squared scores reaching 94.12% and 94.11% for post-treatment visceral fat, and 71.15% and 70.48% for post-treatment subcutaneous fat predictions. Feature selection techniques are also explored to identify critical predictors within the fat prediction models, with BMI, waist circumference, and pretreatment fat levels identified as significant predictors. The study highlights the effectiveness of hyperparameter optimization in refining fat prediction models and offers insights for advancing non-invasive fat reduction methods. The research has implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in body contouring. The study also addresses challenges in fat prediction, including data heterogeneity, dynamic nature of body composition, and the need for large, high-quality datasets. The research gap lies in the precision and personalization of existing models for predicting treatment effects. The study proposes a framework for individualized treatment effect estimation and demonstrates the application of hyperparameter optimization techniques to improve model performance. The results show that Hyperopt and Optuna regression models achieved the best predictive performance, with high R-squared scores and low MSE and MAE. The study also discusses feature correlations and the importance of feature selection in improving model performance. The findings underscore the potential of machine learning in predicting obesity and treatment outcomes, with implications for personalized healthcare.This study explores the application of machine learning for enhanced abdominal fat prediction after cavitation treatment. The research investigates the potential of cavitation in non-invasive abdominal fat reduction and body contouring, focusing on optimizing fat prediction models using advanced hyperparameter optimization techniques, Hyperopt and Optuna. The objective is to improve the predictive accuracy of abdominal fat dynamics post-cavitation treatment. A robust dataset with abdominal fat measurements and cavitation treatment parameters is used to evaluate the efficacy of the approach through regression analysis. The performance of Hyperopt and Optuna regression models is assessed using metrics such as mean squared error, mean absolute error, and R-squared score. Results show that both models exhibit strong predictive capabilities, with R-squared scores reaching 94.12% and 94.11% for post-treatment visceral fat, and 71.15% and 70.48% for post-treatment subcutaneous fat predictions. Feature selection techniques are also explored to identify critical predictors within the fat prediction models, with BMI, waist circumference, and pretreatment fat levels identified as significant predictors. The study highlights the effectiveness of hyperparameter optimization in refining fat prediction models and offers insights for advancing non-invasive fat reduction methods. The research has implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in body contouring. The study also addresses challenges in fat prediction, including data heterogeneity, dynamic nature of body composition, and the need for large, high-quality datasets. The research gap lies in the precision and personalization of existing models for predicting treatment effects. The study proposes a framework for individualized treatment effect estimation and demonstrates the application of hyperparameter optimization techniques to improve model performance. The results show that Hyperopt and Optuna regression models achieved the best predictive performance, with high R-squared scores and low MSE and MAE. The study also discusses feature correlations and the importance of feature selection in improving model performance. The findings underscore the potential of machine learning in predicting obesity and treatment outcomes, with implications for personalized healthcare.