With the advancement of Information Technology, cybersecurity is in a continuous state of assessment as cyberthreats are increasingly leading to dangerous risks throughout the world. Cybercriminals often intend to steal, damage, or disrupt digital assets through a wide variety of malicious actions. Among these activities, phishing attacks are specifically conventional, targeting sensitive and valuable information including credit card numbers and passwords through fraudulent tactics. These attacks often involve fake emails, messages, or websites that imitate authentic sources to tempt individuals into revealing their confidential data or clicking on malicious links. To mitigate this growing threat, our paper proposes a nature-inspired algorithm-based solution for detecting phishing URLs, which offers an enhancement to the traditional dependency on multiple machine learning classifiers. By diligently extracting several features, a new phishing detection model has been developed, facilitating the ability to identify and combat these phishing attempts effectively.