Accurate bandgap prediction is very essential for materials discovery, especially in semiconductors and optoelectronic applications. Here, we have compared and assessed machine learning (ML)-based alternatives to Density Functional Theory for the prediction of bandgaps in 2-D materials. ML Techniques that are widely in use are Support Vector Regression (SVR), Random Forest (RF), Multi-Layer Perceptron (MLP), Gradient Boosting Decision Trees (GBDT). In addition to these models, we have also adopted Gradient Boosting Regressor (GBR), and Clustered Gap Predictor (CGP) approaches in our analysis. This enables a comparative study on different ML models for the prediction of bandgaps in 2-D materials, whose data is taken from the C2DB database. For the performance evaluation, we use metrics like \(R^{2}\) score, mean absolute error (MAE) and root mean squared error (RMSE) values. The analysis of feature importance is done to determine the key factors influencing the bandgaps, while correlation heatmaps offer a view into the relationships between material properties. This study demonstrates the importance of machine learning methods as effective tools for discovery of new materials and associated properties.