In recent years, phishing attacks have emerged as a substantial hazard, endangering online businesses and security by exploiting users to divulge sensitive financial information through fraudulent websites. Despite various proposed methods, accurately distinguishing between legitimate and fraudulent sites in real-time remains challenging. This paper provides a new approach to identifying phishing URLs by employing a feature selection approach that integrates Genetic Algorithm and Particle Swarm optimization. This system optimizes feature selection through population initialisation, fitness evaluation, GA operations, and PSO integration, dynamically balancing exploration and exploitation. The objective is to identify significant features for supervised machine learning techniques, enabling precise phishing URL detection. For classification, multiple machine learning classifiers are employed among which XGBoost provided the best results. Experimental results using the hybrid feature selection prove that the machine learning classifier works much better than the prevailing feature selection approaches. This comprehensive approach provides a reliable method for detecting phishing URLs, improving internet security, and reducing the threats associated with phishing attacks.