In the current world, nearly everyone owns a network device, and the continuous data flow among these devices is quite high. The flow of data among these devices contains every segment of information, from personal information to banking transactions related details. This overflow of information in the present world creates opportunities for cybercriminals to exploit vulnerabilities and gain access to sensitive information. By exploiting these vulnerabilities, cybercriminals launch attacks like phishing to steal confidential information from users and gain unauthorized access. This can be handled with the help of an intelligent, machine learning-based phishing attack detection system, which helps in detecting phishing attacks and safeguard information. In this paper, we address this major cybersecurity issue and propose a machine-learning-based phishing attack detection system (Detect-Phish), trained on a benchmark dataset and evaluated against different performance metrics. The proposed model is compared with various existing competing schemes based on accuracy, demonstrating that it has outperformed them with remarkable results.