Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3907 2019 2019 Research Scholar (PT), VTU Research Centre, MCA Dept., Sir M V I T, Bengaluru, India; Assistant Prof., Dept. of MCA, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India Alamma Alamma Research Supervisor, Professor & Head, Dept. of MCA, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, India Manjula Sanjay Koti Research Co-Supervisor, Professor & HOD, MCA Dept., Sir M. Visvesvaraya Institute of Technology, Bengaluru, Karnataka, India C. H. Vanipriya In India, vector-borne illnesses are becoming a bigger problem.  Because the government still faces difficulties in preventing and controlling these vector-borne illnesses, they have become a burden on society.  Every year, a sizable section of India's population contracts this illness.  Due to the difference in geographical and living standard of people, it becomes difficult to regulate these diseases at early stages in the present system. The main aim of the proposed research works was to design and developing a novel hybridized Kyasanur Forest Disease (KFD) prediction model that leverages a combination of rejuvenated machine-based learning model to enhancing seasonal forecasting & detection of vector-borne diseases. By integrating advanced algorithms such as SVM, NB, LR & Multi-layer perceptron, the research seeks to improving of the accuracy & reliabilities of the prediction related to KFD cases. This hybridized approach aims to better capture the complex relationships between seasonal factors, disease symptoms, and environmental conditions, thereby providing a more effective tool for early detection and management of KFD. 2025 2025 147 167 10.54216/JCIM.160211 https://www.americaspg.com/articleinfo/2/show/3907