PURPOSE: This study compares the performance of two no-code machine learning platforms, Google's Teachable Machine (TM) (Google LLC, Mountain View, CA, USA) and Apple's Create ML (Apple Inc., Cupertino, CA, USA), alongside a traditional deep learning model, ResNet200d, in classifying optic nerve fundus images into glaucoma and non-glaucoma categories using the ACRIMA dataset. METHODS: A comparative cross-sectional analysis was conducted using 705 labeled fundus images from the ACRIMA dataset (326 glaucomatous, 239 non-glaucomatous). Models were trained separately on each platform, and a validation set comprising 70 glaucomatous and 70 non-glaucomatous images was used to assess performance. Performance metrics, such as sensitivity, specificity, F1 score, and Cohen's kappa, were assessed with 95% confidence intervals. Statistical analysis was performed using DATAtab (DATAtab e.U. Graz, Austria (https://datatab.net)). RESULTS: The ResNet200d model demonstrated the highest performance, with an accuracy of 99.29%, a sensitivity of 98.57%, a specificity of 100%, and an F1 score of 99.29%. Create ML achieved a sensitivity of 93.24%, a specificity of 98.48%, and an F1 score of 95.83%. TM exhibited a sensitivity of 95.71%, a specificity of 94.29%, and an F1 score of 95.04%. Both no-code platforms demonstrated strong performance, with Create ML excelling in specificity and TM showing higher sensitivity. CONCLUSION: While the ResNet200d model outperformed both no-code platforms in diagnostic accuracy, the no-code platforms demonstrated robust capabilities, highlighting their potential to democratize artificial intelligence (AI) in healthcare. These results highlight the potential of no-code platforms for democratizing medical image analysis, especially in resource-limited contexts. Further studies with diverse datasets are recommended to validate these results.