This review discusses the application of machine learning (ML) techniques in cancer prognosis and prediction. ML methods, such as Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs), have been widely used in cancer research to develop predictive models for cancer susceptibility, recurrence, and survival. These techniques can analyze complex datasets to identify key features and improve the accuracy of cancer prediction. However, the application of ML methods in clinical practice requires proper validation to ensure their reliability. The review highlights the importance of integrating diverse data sources, including clinical, genomic, and imaging data, to enhance the predictive power of ML models. It also discusses the challenges associated with data quality, preprocessing, and the selection of features. The study emphasizes the need for large, representative datasets and robust validation methods to ensure the generalizability of ML models in clinical settings. The review presents case studies on the use of ML techniques for predicting cancer susceptibility, recurrence, and survival. For example, an ANN-based model was used to predict breast cancer susceptibility by analyzing mammographic findings and demographic data. Another study used a combination of BNs and feature selection algorithms to predict the recurrence of oral squamous cell carcinoma (OSCC). Additionally, an SVM-based model was developed to predict breast cancer recurrence, demonstrating improved accuracy compared to other models. The review also discusses the limitations of current ML approaches, including the small sample sizes and potential biases in data selection. It highlights the need for further research to improve the accuracy and generalizability of ML models in cancer prediction and prognosis. Overall, the review underscores the potential of ML techniques in advancing cancer research and improving patient outcomes through more accurate and personalized predictions.This review discusses the application of machine learning (ML) techniques in cancer prognosis and prediction. ML methods, such as Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs), have been widely used in cancer research to develop predictive models for cancer susceptibility, recurrence, and survival. These techniques can analyze complex datasets to identify key features and improve the accuracy of cancer prediction. However, the application of ML methods in clinical practice requires proper validation to ensure their reliability. The review highlights the importance of integrating diverse data sources, including clinical, genomic, and imaging data, to enhance the predictive power of ML models. It also discusses the challenges associated with data quality, preprocessing, and the selection of features. The study emphasizes the need for large, representative datasets and robust validation methods to ensure the generalizability of ML models in clinical settings. The review presents case studies on the use of ML techniques for predicting cancer susceptibility, recurrence, and survival. For example, an ANN-based model was used to predict breast cancer susceptibility by analyzing mammographic findings and demographic data. Another study used a combination of BNs and feature selection algorithms to predict the recurrence of oral squamous cell carcinoma (OSCC). Additionally, an SVM-based model was developed to predict breast cancer recurrence, demonstrating improved accuracy compared to other models. The review also discusses the limitations of current ML approaches, including the small sample sizes and potential biases in data selection. It highlights the need for further research to improve the accuracy and generalizability of ML models in cancer prediction and prognosis. Overall, the review underscores the potential of ML techniques in advancing cancer research and improving patient outcomes through more accurate and personalized predictions.