Fetal electrocardiography (FECG) is a valuable tool in prenatal monitoring since it provides crucial information about fetal cardiac health and allows for the early diagnosis of congenital heart disease. However, the success of FECG analysis is strongly dependent on overcoming challenges such as noise interference, maternal ECG overlap, and low signal-to-noise ratios. This investigation focuses on recent advances in signal processing techniques that solve these problems while improving the accuracy and reliability of FECG monitoring. Advances in signal capture, noise reduction, and feature extraction are discussed, as well as the use of machine learning and deep learning algorithms to improve diagnostic results. The review also analyzes how these advancements affect real-time monitoring systems, wearable devices, and individualized prenatal care. This paper is a comprehensive resource for academicians, clinicians, and interdisciplinary scholars wishing to understand and contribute to the field of fetal ECG analysis, as it examines both technical advancements and their clinical implications. It also outlines present limitations and potential opportunities, paving the path for more effective and accessible prenatal monitoring systems.