Machine learning is a sub-area of artificial intelligence. It finds its relevance significantly in changing daily routines, interactions, experiences, and decision-making in different fields, for instance, health, lifestyle, agriculture, and finance. This research explored the application of machine learning-based algorithms to identify stress from social media posts with an emphasis on text classification through the platform Reddit. Experimental methodology: A study of employment algorithms like Naive Bayes and Support Vector Machine (SVM) alongside data preprocessing and feature extraction techniques such as Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). The results indicate Naive Bayes to have a higher accuracy than SVM which was at 67% while the former at 73.9%, demonstrating the possibility of employing models for automated stress detection. Such findings stress the importance of machine learning when it comes to mental health monitoring applications and the use of social media for identifying and addressing stress-related problems.