Aim:The aim is to improve the detection of mushroom insalubrity based on features extracted from images by using K-Nearest Neighbour (KNN) algorithm compared with Support Vector Machine (SVM) algorithm. Materials and methods: By using K-Nearest Neighbour algorithm and SVM algorithm both were identified and performed with the sample size of 45 each and the software tools that were used in this project are a jupyter notebook. Accuracy values for identification of toxicity in mushrooms are calculated to quantify the performance of K-Nearest Neighbour algorithm. Results and Discussion : The analysis on train dataset and test dataset were successfully performed using SPSS and acquired accuracy for the Convolutional neural network compared to K-nearest neighbor algorithm method which gave more accuracy with the level of significance (p<0.05) and the resultant data depicts the reliability in independent sample tests. Conclusion: On the whole process of prediction of accuracy the K-nearest neighbour algorithm gives significantly better performance compared with Convolutional neural network.