Background/Aims: This study aimed to develop a diagnostic tool using machine learning to apply functional luminal imaging probe (FLIP) panometry data to determine the probability of esophagogastric junction (EGJ) obstruction as determined using the Chicago Classification version 4.0 (CCv4.0) and high-resolution manometry (HRM). Methods: Five hundred and fifty-seven adult patients that completed FLIP and HRM (with a conclusive CCv4.0 assessment of EGJ outflow) and 35 asymptomatic volunteers (“controls”) were included. EGJ opening was evaluated with 16-cm FLIP performed during sedated endoscopy via EGJ-distensibility index and maximum EGJ diameter. HRM was classified according to the CCv4.0 as conclusive disorders of EGJ outflow or normal EGJ outflow (timed barium esophagram applied when required and available). The probability tool utilized Bayesian additive regression treesBART, which were evaluated using a leave-one-out approach and a holdout test set. Results: Per HRM and CCv4.0, 243 patients had a conclusive disorder of EGJ outflow while 314 patients (and all 35 controls) had normal EGJ outflow. The model accuracy to predict EGJ obstruction (based on leave-one-out/holdout test set, respectively) was 89%/90%, with 87%/85% sensitivity, 92%/97% specificity, and an area under the receiver operating characteristic curve of 0.95/0.97. A free, opensource tool to calculate probability for EGJ obstruction using FLIP metrics is available at https://www.wklytics.commgi/prob_flip.html. Conclusions Application of FLIP metrics utilizing a probabilistic approach incorporates the diagnostic confidence (or uncertainty) into the clinical interpretation of EGJ obstruction. This tool can provide clinical decision support during application of FLIP Panometry for evaluation of esophageal motility disorders.