Accurate placement of nasogastric (NG) tubes is crucial to ensure effective treatment and avoid potential complications. Conventional methods of confirming NG tube placement, such as auscultation and pH testing, can be prone to errors. This study explores the use of auscultation signals in conjunction with machine learning acoustic feature modelling to verify NG tube placement. Study participants were recruited from hospital inpatients who had NG tubes inserted for relevant medical indications, and underwent radiographic imaging for confirmation of NG tube placement. Eligible patients were recruited into the study in either the gastric (NG tube in stomach) or esophageal (NG tube removed with tip lying in esophagus at 30cm mark) phase, or both. Audio signals were obtained from two locations: left chest and epigastrium, during insufflation of 50 millilitres of air for each phase. Building upon on a pre-trained model, we extracted acoustic signatures and transfer-learned VGGish pretrained network model to predict the NG tube placement. A total of 103 audio file pairs (left chest and epigastrium) were obtained from 61 patients, of which 74 audio pairs (44 gastric and 30 esophageal phase) were utilised for training and validation of the prediction model. An accuracy of 89.3% was obtained for the epigastrium sensor to correctly predict the NG tube location, with a sensitivity of 81.7% and specificity of 94.4%, when aggregated over 10 runs of repeated random subsampling. These results indicate acoustic features, combined with machine learning techniques, offer promise for verifying the NG tube placement. Further refinement and validation of this method in clinical settings are recommended for its integration into standard medical practice.