Satiation, the process that regulates meal size and termination, varies widely among adults with obesity. To better understand and leverage this variability, we assessed calories to satiation (CTS) through an ad libitum meal, combined with physiological and behavioral evaluations, including calorimetry, imaging, blood sampling, and gastric emptying tests. Although factors like baseline characteristics, body composition, and hormone levels partially explain CTS variability, they leave substantial variability unaccounted for. To address this gap, we developed a machine-learning-assisted genetic risk score (CTS) to predict high CTS. In a randomized clinical trial, participants with high CTS or CTS achieved greater weight loss with phentermine-topiramate over 52 weeks, whereas those with low CTS or CTS responded better to liraglutide at 16 weeks in a separate trial. These findings highlight the potential of combining satiation measurements with genetic modeling to predict treatment outcomes and inform personalized strategies for obesity management.