Varying deep learning models were evaluated for their accuracy to decipher gut microbiome data and its connection to conditions like irritable bowel syndrome (IBS). Statistical and machine learning models such as XGBoost, LightGBM, Random Forest, logistic regression, and decision trees were used to compare with the performance of the proposed deep neural network (DNN). The dataset was divided into training and testing levels in a 70:30 ratio and performance was evaluated using F1-score (a metric that combines precision and recall into a single value), recall, accuracy, and precision. The DNN outperformed the other deep learning models and its performance was narrowly close to the logistic regression. Specifically, the DNN demonstrated the highest accuracy of 92.79%, precision of 91.50%, recall of 90.95%, and F1-score of 91.82%. Despite the close numbers on paper, the authors performed 100 rounds of cross-validation and results show that the DNN is statistically better than the logistic regression because it can model different types of interactions among microbial taxa in the data.