HPV type categorization is essential for early detection and determining the risk of developing HPV-related cancers. The novelty of the proposed work is an intelligent system, HPVC 1.0, that uses ML and DL approaches to classify HPV types in different classes by exploiting gene size features obtained from viral genomes of various strains of HPV genotypes. SMOTE and ADASYN address the class imbalance issue in the dataset. Altogether, we evaluated eight models in the following categories: machine learning (ML) algorithms such as Support Vector Machine (SVM), Logistic Regression, Random Forest, K-Nearest Neighbour(KNN), Decision Tree and deep learning (DL) models including Artificial Neural Networks(ANN), Convolutional Neural Networks(CNN), Long Short-Term Memory(LSTM) and Gated Recurrent Unit(RU). Performance HPVC 1.0 is expressed in accuracy, F1-score, sensitivity, specificity, and ROC-AUC. Results show that SVM and Logistic Regression are more stable classifiers on small datasets, whereas the Random Forest model and CNN possess higher accuracy in larger balanced datasets. This HPVC 1.0 system provides a scalable and efficient approach to the classical HPV type identification, with the potential applications for real-time clinical diagnosis and public health screening programs