Caenorhabditis elegans (C. elegans) is a microscopic, free-living nematode widely used as a model organism for studying fundamental biological processes, including development. Moreover, because of its rapid growth and simple maintenance, C. elegans is widely used in high-throughput screening studies. However, conventional methods for analyzing these morphological and developmental characteristics often rely on manual microscopy and human evaluations. These methods are labor intensive, slow, prone to mistakes, and not easy to scale up, particularly for high-throughput studies where vast amounts of information are generated. To solve these problems, researchers can bypass these methodologies by employing machine learning which can perform consistent and error-free data processing. This review analyses how various machine learning methods have been employed to counteract the problems faced in traditional experimental approaches. Their impact on the enhancement of precision, effectiveness, and scalability of developmental studies in C. elegans has been discussed, as well as the issues that pose constraints to the adoption of these technologies in low-resource laboratories.