The aim of this research is to evaluate the efficiency of the Machine Learning (ML) models and Deep Learning (DL) models for automatic identification of basic elements of the architectural world of Islam. The study is carried out on limite d Islamic architecture images of 2000 images, covering five different classes (common features) of architecture. The study is d one on a set of selected 2,000 images from various categories of prevalent features found in Islamic architecture. Works attentiv e to the various feature extraction approaches such handcrafted methods as, Histogram of Oriented Gradients (HOG), Local Binary Pa tterns (LBP) to automated feature learning using Convolutional Neural Networks (CNNs) are compared. Both Support Vector Machines (SVM) and Random Forest (RF) models and different CNN models, namely EfficientNetB0, were trained and tested following standard pre -processing (grayscale conversion, resizing to 64×64 coordinates, and augmentation). The results clearly show that DL models outperform the best traditional model (SVM with a Radial Basis Function (RBF) kernel) with 85% accuracy as best, surpassing the 83% accuracy level attained by the other traditional models. The results also illustrate the significant performance improvement provided by DL models, and they have managed to improve their accuracy by 12 points compared with the best traditional model (SVM with an RBF kernel, achieving 85%). In the Islamic architecture domain, which has less previous ML application, the novelty of this study is the specific evaluation of classical and DL methods for the classification of Islamic architecture elements. Th e results validate the efficacy of CNNs for automatically learning discriminative features, laying important groundwork related to architectural heritage classification and paving the way for future studies .

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