Among all endocrine malignancies, thyroid cancer is the common endocrine malignant in our body. Thyroid cancer can be developed from two types of cells where the first type of cell is follicular epithelial cell, and the second type of cell is parafollicular C cells. Differentiated Thyroid Cancer (DTC), which originates from follicular epithelial cells, is the primary form of thyroid gland cancer. Thyroid cancer is one of the endocrine malignant which has high recurrence rate after initial treatment. So, it is important to predict thyroid cancer recurrence efficiently. In this research, “Differentiated Thyroid Cancer Recurrence” dataset was collected from UCI machine learbning repository where 383 instances and 17 attributes were present. The target was to develop a machine learning (ML) model which can predict the Differentiated Thyroid Cancer Recurrence effectively and efficiently. This research developed a ML model by using Random Forest (RF) Classifier where it performed best among used other ML models. From evaluation metrics, Random Forest obtained the highest testing accuracy and also \(F_1\) -score where the testing accuracy was 98.70% and \(F_1\) -score was 97.30%. To evaluate further, this research used average cross-validation accuracy, precision, recall, and ROC-AUC curve, where average cross-validation accuracy on the training set was 96.08%.