The most common vector-borne illness in the world is dengue. Medical experts’ primary goal is to reduce mortality rates by promptly diagnosing and treating dengue. In this study, we suggest an autonomous cycle that combines data analytic activities to aid in clinical dengue management decision-making. The autonomous cycle supports the diagnosis and treatment of dengue in particular. Artificial neural networks and support vector machines were used in the construction of the suggested system for classification tasks, while a genetic algorithm was used in evolutionary computing for prescription tasks (treatment). With the aid of dengue patient datasets submitted by healthcare facilities, the system was statistically assessed. We used qualitative criteria to evaluate our technology against earlier research. The suggested system can categorize a patient’s clinical profile and suggest the most effective course of therapy. A genetic algorithm specifically recommends treatment options for specific patients and classifies dengue with 98% accuracy. Finally, the adaptability and flexibility of our system will enable the addition of new tasks for dengue analysis.