In the dynamic domain of cybersecurity, zero-day exploits represent a critical and persistent challenge to digital security for both individuals and organizations. These exploits exploit vulnerabilities not yet known to software vendors or defenders, necessitating advanced detection and response strategies. This review paper explores the role of machine learning (ML) as an essential instrument in the detection and neutralization of zero-day threats. It assesses a variety of ML algorithms and methodologies applied in recent research, delineating their effectiveness and limitations for zero-day exploit detection. The analysis spans from conventional statistical models to cutting-edge deep learning techniques, scrutinizing their adaptability to the evolving landscape of cybersecurity threats. Additionally, this paper discusses the synergy between ML techniques and other cybersecurity measures to forge comprehensive defense mechanisms capable of identifying and counteracting these threats efficiently. It addresses significant challenges such as data paucity, high false positive rates, and the ongoing adversarial contest with cyber attackers. Concluding with insights into prospective research avenues and advancements, this paper highlights the imperative for relentless innovation to outpace sophisticated cyber adversaries. Aimed at researchers, cybersecurity practitioners, and policymakers, this review offers an in-depth examination of leveraging machine learning to confront zero-day exploits.