This chapter examines the prediction of viral host tropism and cross-species infections, focusing on analytical methods and algorithms that aid in understanding virus-host interactions and the potential for interspecies transmission. It discusses the use of feature extraction and machine learning techniques, such as support vector machines (SVMs) and random forests (RFs), to predict host specificity and assess the likelihood of cross-species viral infections. It highlights two case studies: one on predicting the host tropism of influenza viruses and another on predicting zoonotic viral infections. Through these case studies, the chapter demonstrates how machine learning models can process large datasets to predict viral transmission risks, offering valuable insights for surveillance and public health strategies. It also emphasizes the role of computational frameworks in predicting cross-species infection risk and the importance of performance evaluation and optimization for improving model accuracy. The chapter concludes by addressing the future of predictive methods in viral dynamics, urging continued research to refine these tools and better prepare for emerging infectious diseases.