A leading widespread upsurge was identified as a severe acute respiratory syndrome (SARS) coronavirus “COVID-19.” It has blowout a global threat to human existence, throughout the world with millions of recognized instances and griefs, originating from Wuhan, China, to every other part of the universe by demeaning activities put in place to regulate it. Various actions are ongoing to combat the increase of this dangerous disease, such as health precautions and measures, money, substructures, databases, protective devices, and medications, among other necessities, yet interminable upsurge of the disease post a constant interruption to the universe. Several widespread innovative forecast methods have emerged in predicting COVID-19 globally to obtain keen results and impressive, relevant preemptive procedures. This study aims to apply a machine learning method for prediction of COVID-19 incidence, using KPCA-SVM. Its objectives are to use recent cases and their gene data, by imploring KPCA to fetch relevant latent components. The reduced output is classified and evaluated in terms of the performance metrics. This study is implemented in MATLAB. The algorithms used for the prediction are KPCA and SVM. The results are evaluated using accuracy, sensitivity, specificity, F-score, Matthews correlation coefficient, precision, and negative predictive value. This study uses the KPCA to fetch relevant information for the enormous data and classified using the L-SVM and SVM-RBF; it achieved 93% and 87% accuracy, respectively. The necessity to identify suitable prognostic suggestions for COVID-19 must regulate the difficulties in apprehending the disease’s increase. In this investigation, a machine learning prediction approach is projected for COVID-19 to convey the importance of principal frameworks for enhancements and evolving quicker and capable conduct for evaluating, classifying, and predicting health status concerning actions and symptoms observed, to help healthcare persons recognize and record incidence to verify qualified healthcare across nations.