This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, focusing on enhancing the predictive accuracy of abdominal fat dynamics post-treatment. The researchers used advanced hyperparameter optimization techniques, Hyperopt and Optuna, to optimize fat prediction models. A robust dataset with abdominal fat measurements and cavitation treatment parameters was employed to evaluate the efficacy of the approach through regression analysis. The performance of the models was assessed using metrics such as mean squared error (MSE), mean absolute error (MAE), and R-squared score. The results showed that both Hyperopt and Optuna regression models exhibited 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, respectively. Feature selection techniques identified key predictors such as BMI, waist circumference, and pretreatment fat levels. The study highlights the effectiveness of hyperparameter optimization in refining fat prediction models and offers valuable insights for advancing non-invasive fat reduction methods. The findings have important implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in body contouring.This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, focusing on enhancing the predictive accuracy of abdominal fat dynamics post-treatment. The researchers used advanced hyperparameter optimization techniques, Hyperopt and Optuna, to optimize fat prediction models. A robust dataset with abdominal fat measurements and cavitation treatment parameters was employed to evaluate the efficacy of the approach through regression analysis. The performance of the models was assessed using metrics such as mean squared error (MSE), mean absolute error (MAE), and R-squared score. The results showed that both Hyperopt and Optuna regression models exhibited 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, respectively. Feature selection techniques identified key predictors such as BMI, waist circumference, and pretreatment fat levels. The study highlights the effectiveness of hyperparameter optimization in refining fat prediction models and offers valuable insights for advancing non-invasive fat reduction methods. The findings have important implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in body contouring.