Frog species across the world are vulnerable and in decline, despite the fact that frogs are an integral part of biological systems. Within this enchanting world, we encounter two intriguing groups: non-poisonous and poisonous frogs. This phenomenon prompts the development of an automated computer vision-based frog detection system that can distinguish between deadly and non-poisonous frogs, leading to the development of early treatment methods and a reduction in relative economic loss. In this study, we present a convolutional neural network-based technique for frog detection. The CNN model required numerous epochs to run in order to provide the best result. However, we must also consider the trade-off in convergence speed. In our exploration, we conducted experiments with different epochs. Interestingly, our findings revealed that running the model for 30 epochs yielded the highest accuracy, reaching an impressive 90.83%. Through rigorous and thorough experimentation, we evaluated confusion metrics and discovered that they yielded exceptional results.