The rapid advancement of deep neural networks (DNNs) has significantly transformed various sectors, demonstrating unparalleled proficiency in managing intricate tasks in multiple domains. This progress holds promising implications for fetal monitoring systems, particularly in leveraging artificial intelligence (AI) to enhance the detection of abnormalities through cardiotocography (CTG). Understanding the evolving role of AI in obstetrics and gynecology, and its potential to revolutionize clinical practices, is crucial. This paper provides a comprehensive review of the integration of computerized cardiotocography and its associated technologies, coupled with a detailed analysis of the latest developments in machine learning (ML) and deep learning (DL) within the realm of CTG research over recent years. It further explores additional tools for fetal heart monitoring, such as fetal cardiotocography, and delves into the implications of these advancements. Moreover, the study outlines prospective research avenues, discusses innovative AI approaches to overcome existing challenges, and envisages the transformative impact of AI on the future of antepartum and intrapartum care, paving the way for a new era of automated fetal monitoring.