Predicting the psychological vulnerability of patients with cancer using machine learning algorithms is one of the emerging approaches in psycho-oncology to identify patients at risk of psychological distress. Psychological vulnerability in cancer refers to a set of personal, emotional, cognitive, social, and clinical factors that can increase the likelihood of anxiety, depression, distress, adjustment disorder, fear of recurrence, and decreased quality of life. In the traditional approach, the identification of this vulnerability is mainly done through clinical interviews, expert observation, and standard screening tools, but the high volume of clinical and psychological data has made it possible to use advanced computational methods. Machine learning can identify patterns associated with increased risk of distress by simultaneous analysis of variables such as age, gender, type and stage of cancer, severity of symptoms, history of psychological problems, sleep quality, pain, fatigue, social support, economic status, coping style, and quality of life. Algorithms such as logistic regression, decision tree, random forest, support vector machine, and neural network-based methods have different capabilities for classifying and predicting risk. In this process, the goal is not simply to create a statistical model, but to develop a tool that can identify patients needing a more accurate psychological assessment at an early stage. The potential benefit of this approach is the move from reactive care to preventive, targeted care in psycho-oncology. If the model can identify the likelihood of distress before symptoms worsen, the treatment team can begin psychological assessment and supportive interventions sooner. This is especially important during critical periods such as diagnosis time, start of chemotherapy, radiation therapy, surgery, and post-treatment follow-up. Machine learning models can also allow classifying patients based on different levels of risk and designing interventions tailored to their needs. However, psychological risk prediction is different from definitive prediction of a patient's future condition, and the algorithm's output should be interpreted as probability or risk estimate. One of the most important requirements for developing such models is the use of valid, adequate, and representative data from the target population. Incomplete, unbalanced, or biased data can reduce model accuracy and create inequities in patient identification. Cultural, linguistic, and social differences can also affect the way psychological symptoms are expressed and reported and should be considered in model design. On the other hand, the use of sensitive mental health and cancer information increases the need for confidentiality, data security, and informed patient satisfaction. Another important challenge is the interpretability of algorithms, as the expert must know what factors the model has placed a patient in the high-risk group. Complex models whose output cannot be explained may have limited acceptance in the clinical setting. The use of explanatory AI methods can help clarify the factors affecting the prediction. Also, the performance of a model should be validated in independent populations and different treatment centers and not judged solely on its accuracy in educational data. Indicators such as sensitivity, specificity, accuracy, area under the ROC curve, and calibration are important for evaluating model performance. In psycho-oncology, the sensitivity of the model is particularly important for identifying vulnerable patients, as failure to identify a patient in need of support may eliminate the opportunity for early intervention. However, an over-identification of high-risk cases can also lead to unnecessary referrals and an increase in the burden of the treatment system. Therefore, the selection of the appropriate threshold for risk classification should be based on the clinical objective and its actual outcomes. Machine learning can also be used in combining longitudinal data and investigating changes in the patient's mental state over time. Analyzing the trend of change can provide a more accurate picture of psychological status than a single assessment. In the future, combining questionnaire data with clinical information, data recorded in electronic records, and behavioral indicators can increase the predictive capacity of models. However, the use of multi-source data requires standardization, quality control, and rigorous privacy protection mechanisms. From a clinical perspective, the machine learning model should not replace expert judgment, but should be used as a decision-support tool alongside psychological evaluation. The psycho-oncology psychologist can interpret the output of the algorithm along with the interview, clinical observation, and unique circumstances of the patient. The patient should also know that the algorithmic evaluation is a probabilistic estimate and should not be considered as a definitive diagnosis of a psychological disorder. In addition to predictive accuracy, research should also examine the clinical implications of using the model, for example, whether early detection actually leads to a reduction in distress, anxiety, or depression and an improvement in quality of life. Attention to AI ethics, algorithmic justice, transparency, informed consent, and professional responsibility should also be part of the development process. By identifying hidden patterns in clinical and psychological data, machine learning can shift the capacity of psycho-oncology from late identification of problems to early prediction and intervention. The main value of this technology is realized when predictive models are placed alongside human expertise, clinical assessment, and evidence-based interventions, with the ultimate goal of improving the quality of care and quality of life of patients.