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Reliable and continuous psycho-physical symptoms monitoring is essential to improve cancer patients' quality of life. Scales and questionnaires, traditionally used in clinical contexts, provide valuable information regarding patients' overall well-being. However, their intrinsic characteristics do not allow continuous symptoms monitoring. The integration of wearable sensors and artificial intelligence algorithms promises to revolutionize health monitoring, providing continuous and pervasive recordings. In this study, we assessed the performance of machine learning (ML) algorithms to predict the presence of nine common symptoms experienced by cancer patients, using physiological signals and self-rated symptoms in real-world context, i.e. at patient's home. Features were extracted from electrodermal activity (EDA), skin temperature (TEMP), and accelerometer (ACC) data. A principal component analysis was implemented to merge the extracted features, selecting the first components, describing the 90% of the total variance, to feed three ML algorithms - logistic regression (LogReg), support vector machine (SVM), and random forest (RF). A bootstrap approach was used to enhance the robustness of the results. SVM and RF provide consistently better performance compared to LogReg, achieving a better balance across the evaluated performance metrics. Tiredness achieved the highest F1-score (91.68% ± 2.81%) with SVM. Other symptoms such as malaise, drowsiness, anxiety, appetite, nausea and pain, achieved F1-scores above 70%.Despite the limitation of a small sample size and not accounting for the time of the day, these preliminary findings suggest the feasibility of such an approach, having the potential of improving cancer patient care.Clinical Relevance- The integration of wearable devices and machine learning offers a promising solution for continuous psycho-physical symptoms monitoring, enabling early intervention and personalized treatment strategies.