One of the major obstacles to the widespread adoption of immersive eXtended Reality (XR) is cybersickness, a form of motion sickness. Typically, cybersickness is detected through explicit methods such as self-reported questionnaires, which are not ideal for online and implicit monitoring of users' well-being. This study tackles the challenge of implicitly detecting and measuring cybersickness in Virtual Reality (VR) environments Through physiological signals.A multimodal approach that integrates physiological signals with self-reported measures is proposed to achieve this. The study utilized a mixed-methods design that combined quantitative and qualitative analyses, using a roller coaster simulation as an experimental paradigm. The research analyzed physiological data collected from a group of 22 participants through Repeated Measures Analysis of Variance (RMANOVA) and machine learning algorithms, including an XGBoost predictive model. The physiological markers studied include Electroencephalograms (EEG), Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Temperature (TMP) sensors. The study discovered significant correlations between high Simulator Sickness Questionnaire (SSQ) scores and physiological measures such as brain rhythms, engagement, visual fatigue, and drowsiness indices, as well as heart rate fluctuations. The XGBoost model achieved an impressive 86.66 percent accuracy in detecting elevated cybersickness symptoms. Further validation using Explainable AI (XAI) techniques confirmed the elevated levels of drowsiness and reduced engagement in participants experiencing elevated cybersickness. By providing a comprehensive, multimodal approach for quantitative assessment, this study fills a gap in the existing literature and paves the way for the development of adaptive systems that modulate their behavior based on real-time physiological data.