The digital landscape has experienced significant advancements in recent years, particularly in the realm of the Internet, which has become increasingly integral to our daily activities. Consequently, the threat of cyber-attacks is rapidly escalating, as malicious actors employ sophisticated tactics to exploit vulnerabilities. Among the most critical dangers are malicious URLs, which are crafted to surreptitiously extract sensitive information, often by deceiving unsuspecting users. This can lead to compromised systems and enormous financial losses, amounting to billions of dollars yearly. Consequently, the imperative to safeguard websites has never been higher. This study evaluates the literature on machine learning models with a focus on malicious URL identification techniques, examining the limitations of previous research as well as detection techniques, feature types, and datasets.