The proposed framework advocates the early detection and diagnosis of disease and increases the efficiency of treatment and recovery. The data collected from wearable devices are processed in a fuzzy inference system (FIS) which consists of four main components namely fuzzification, Knowledge Discovery Dataset (KDD), prediction model, and defuzzification. The fuzzification data and KDD outcome are forwarded to the prediction model to train the model to detect the disease. The inference system’s final component is defuzzification, which converts the fuzzy set from the prediction model to the desired crisp set values. The proposed research exploits Weka 3.8.6 tool to analyze the monkeypox dataset. A fuzzy decision tree is generated from the J48 algorithm and eight fuzzy rules are formulated from the C4.5 algorithm. The devised research uses approximately 25000 instances in which 16682 instances are classified correctly 66.728 % and 8318 instances are classified incorrectly 33.272 %. The performance metrics like True Positive (TP) rate, False Positive (FP) rate, precision, recall, and F-measure are evaluated. The confusion matrix predicts the number of correct and incorrect predictions. The suggested fuzzy logic-based analytics advocates the faster detection of disease and reduces hospitalization. Also, the research promotes effective interaction among healthcare service providers and patients. The devised work is a research-driven exploration into the application of fuzzy logic in the domain of medical diagnosis and prognosis.