Mushroom poisoning is one of the phenomena that occur in society due to errors in identifying mushrooms and eating them. The effects are varied, ranging from mild cases, such as nausea, vomiting, diarrhea, dizziness, to the more severe case, namely death. As part of efforts to minimize the occurrence of similar incident, a solution is created through the approach of artificial intelligence, which is to generete a model that can distinguish toxic mushrooms and non-toxic one. There are five species of mushroom that is carried out in this research, those are Amanita caesarea, Cantharellus cibarius, and Volvariella volvacea as non-toxic mushrooms and Amanita phalloides and Omphalotus olearius as toxic mushrooms. There are 142 total images used in this research. For training data, there are 100 images, with 20 images for each species. For validation data, there are 37 images in total, with 7 images for each species, except for species V. volvacea, which has 9 images. And, as for test data, one image for each species. The model was created using Convolutional Neural Network (CNN). The final model has an accuracy rate of 78%. During the training process, data augmentation techniques are used, which is useful to reproduce the same image, but different from original image, by using some transformations, such as rotation, horizontal and vertical flip, adding noise, affine transform, blurring, and center crop. There are 7 images generated from the data augmentation process plus one original image. The batch size used during training phase is 32. Most prediction error are caused by V. volvacea, where 9 images used as validation in total, only 3 images are predicted to be correct, the rest are predicted to be A. phalloides. Overall, the model’s performance is quite good in classifying the species of A. caesarea, A. phalloides, C. cibarius, and O. olearius but biased against V. volvacea. Therefore, the model produced in this research is not reliable enough to be applied to the wider community as a “tool” to distinguish these mushrooms. Keywords: A. caesarea, A. phalloides, C. cibarius, O. olearius, V. volvacea, Deep Learning, CNN