Microfossils provide evidence and knowledge about the Earth, life, ecology, and biological changes, and one way to access this knowledge is through the classification of microfossil species. In traditional classification methods, paleontologists analyze each microfossil sample in detail manually under a microscope. This operation not only needs extensive time for processing, but is expensive and requires well-trained experts. In recent years, this problem has been tackled through the use of automatic classification methods that benefit from machine learning and imaging techniques. Since the costs of scanning electron microscopy and micro-computer tomography systems are quite high, we focused on obtaining high performance using a low-cost light microscopic imaging system. In this paper, we have proposed a highly effective and low-cost classification system using deep learning and shallow machine learning algorithms to identify Globotruncana species. The deep model defines the data using high-level layers, whereas the shallow models recognize low-level layers during the learning process. Initially, we collected more than 180 microscopic images for three different Globotruncana species and labeled them individually. Afterwards, we applied well-known machine learning methods to this dataset and analyzed the results in detail. Experimental results showed that Globotruncana species can be classified by deep and shallow machine learning algorithms with the high accuracy of up to 100% and 100%, respectively. The performance of the classification methods was also compared in terms of their accuracy, sensitivity, specificity, precision, and F-scores.