Stress, a prevalent condition with wide-ranging implications for health and well-being necessitates accurate and timely detection to enable effective intervention and support. This article explores the use of various bio-signals, including thermal, electrical, and impedance measurements, to detect stress and mitigate associated health conditions. Specifically, the study focuses on analyzing Internet of things (IoT) sensed data in conjunction with machine learning (ML) algorithms. The research examines the impact of stress detection by employing techniques such as heart rate readings, electrocardiogram (ECG), pulse sensor, and ESP32. The collected datasets are categorized using ML algorithms, including decision tree, K-nearest neighbors (KNN), support vector machines (SVM), and logistic regression (LR). Performance evaluation metrics such as precision, accuracy, F1 score, and recall are utilized to assess the effectiveness of the data analysis. The findings demonstrate the superior performance of decision tree as a stress classifier. This work contributes to the growing body of knowledge on stress detection and highlights the potential for integrating IoT and ML in stress management applications.