The strong influence of technology on our lives reveals information about our online activity and preferences through digital fingerprinting. Understanding their components uses, and advantages are necessary for successfully navigating the digital universe. Approaches like deep learning are employed to find out which algorithm is the best for digital fingerprint analysis. ML algorithms have been used for a long time to make the existence of human civilization easier and more efficient. In this study, we trained techniques and classifiers such as neural networks (NN), support vector machines (SVM), Gaussian Naive Bayes classifier, K-nearest neighbors (KNN), Principal Component Analysis(PCA), Logistic Regression, Neural network, and Random Forest Classifier, and Decision Tree. We learned a method for understanding datasets and implemented several classifiers for analysis with this work. Algorithms used in digital fingerprinting are also discussed based on these techniques.