Seasonal infections in humans encompass a wide range of diseases, from common childhood ailments like measles, diphtheria, and chickenpox, to infections transmitted through the fecal–oral route, such as cholera and rotavirus. Additionally, vector-borne diseases like malaria and even sexually transmitted infections like gonorrhea also exhibit seasonal patterns. Therefore, early and accurate detection of seasonal infections is crucial as it can help in reducing the spread of infection. Numerous researchers have used machine learning approaches to forecast infectious diseases in recent years, and the outcomes have been encouraging. The current work offers a thorough discussion about the advantages, disadvantages, and possibilities for the scientific application of various machine-learning techniques utilized in the detection of seasonal infections. We have used various machine-learning techniques: Random Forest (RF), Naïve Bayes, K-Fold cross-validation, and Support Vector Machine (SVM). We also calculated the accuracy of each model, which helps in predicting the seasonal disease based on the symptoms mentioned.