Determining depression is hugely important because it affects people's wellness. The common practice of diagnosing depression usually involves a physical session with a practitioner, which reduces the possibility of finding patients who are depressed. Instead, they investigate the possibility of detecting depression solely from textual data based on the posts people post on social media profiles. This research work consists of how Reddit users' text messages can be processed to perform a classification task to identify depression. In order to detect depression, we used Word and Sentence Embeddings. Different embeddings are tested with algorithms such as SVM classifiers, KNN classifiers, and decision tree classifiers, and the results are compared to those from various datasets. Evaluation metrics such as accuracy and precision are used to evaluate the performance of classifiers. USE embedding outperformed Word embeddings with a maximum accuracy of 92%, precision of 95%, recall of 96%, and F1 score of 92%. Results from the suggested method are compared to those from other methods and are found to be satisfactory.