The dependence on online shopping makes consumers to popular targets of malicious intents. With a vast understanding of the human psyche, dark patterns are capable of leading consumers to perform actions which they would not do under normal circumstances, such as evoking buying pressure or giving away sensitive data. In this thesis, we focus on the detection of dark patterns, especially the Social Proof, Misdirection, Scarcity, and Urgency patterns using multinomial naïve Bayes, support-vector machine, k-nearest neighbor, and random forest, as well as state-of-the-art transfer learning methods like ULMFiT and DistilBERT. For this purpose, we utilize a collection of 1818 classified dark patterns. First, we perform nested cross-validations for all algorithms for valuable insights that we need for the model selection. Overall we achieve a balanced accuracy of 0.926 on average, whereas all models, except for k-nearest neighbor, perform similarly well. Then, with the gained knowledge, we demonstrate that dark patterns can indeed be detected using machine learning techniques. At last, using our fine-tuned models, we reveal the existence of dark patterns in a collection of newsletter emails, with a performance of 0.436 balanced accuracy. Thus we conclude, that this work provides essential insights into the fact that dark patterns exist in hitherto unnoticed fields and how more sophisticated methods are crucial to combat such patterns.