The modern world’s demands have increased stress levels, impacting both emotional and physical health. Sleep patterns and stress levels are intricately connected. When people encounter stress, it can significantly affect their capacity to initiate sleep, maintain sleep, and attain rejuvenating sleep patterns. Recognizing the significance of managing stress effectively, this paper explores an innovative approach to stress prediction by integrating sleep patterns with Machine Learning (ML) algorithms and a novel approach incorporating ensemble learning. Sleep patterns are crucial indicators of mental well-being, and leveraging this information for stress prediction is explored through the synergistic combination of diverse predictive models within an ensemble framework. Our study delves into applying machine learning-based forecasting models to gauge individual stress levels. Through a systematic exploration, we analyze sleep patterns and the fundamental aspects of sleep assessment to provide a holistic understanding. Investigating advancements in sleep behavior analysis, data collection, and monitoring techniques, we highlight its merits and demerits. Moreover, we address potential research hurdles and avenues for further exploration. Employing F1-score, recall, and accuracy metrics, we compare machine learning models on the sleep dataset. Our findings reveal that the Logistic Regression, Gaussian Naive Bayes, Extra Trees Classifier, and Linear Discriminant Analysis models outshine competitors, achieving perfect F1-scores of 1.0. The findings suggest that the proposed methodology is effective and reliable, highlighting its potential to enhance stress prediction techniques for practical application in real-world situations.