We utilized a modified stethoscope to capture digital recordings, amassing over 500 minutes of real patient data. Alongside these recordings, we diligently documented pertinent metadata from each patient, including specific symptoms, their Bristol scale ratings, and relevant medical antecedents. We employed machine learning algorithms with dual objectives: firstly, leveraging deep learning, we used a sequence of two Convolutional Neural Networks to craft a bowel sound recognition model that boasts an accuracy of 80%; and secondly, utilizing statistical models to evaluate the feature importances in predicting bowel sounds and symptoms. By engineering sound features, we were able to predict individual symptoms. Notably, our findings revealed that certain sound features possess p-values less than 1% when correlated with bowel symptoms. The overarching aim of this research is to unearth potential correlations between bowel sounds, manifested symptoms, and outcomes from colonoscopy examinations. Advancing research on the relationship between bowel sounds and bowel disorders holds the potential to revolutionize gastroenterology.