Classification of fossil images is a crucial task in paleontology, aiding in understanding ancient life forms and their evolutionary history. This study explores the effectiveness of statistical features in representing the texture of six distinct fossil classes: Ammonites, Belemnites, Corals, Crinoids, Leaf Fossils, and Trilobites. This approach will lessen the computing power needed to categorize the fossils and the enormous volume of data usually required to train Artificial Intelligence (AI) models. Various statistical texture features were extracted from the images. Several Machine Learning (ML) classifiers were trained and assessed using these features. Each model’s performance was evaluated using accuracy, precision, recall, and F1−score metrics. This approach has been compared with feature extraction conducted by Convolution Neural Network (CNN). The results demonstrate the effectiveness of the proposed feature extraction approach in classifying fossil images, outperforming the CNN model with 10% accuracy and thus providing insights into the most suitable approaches for automated fossil identification.