The most extensive threat type, for the present moment at least, remains phishing attacks that rely on people’s susceptibility to trick them into sharing personal data. Such attacks generally consist of fake emails originating from an apparently reliable source, for instance, businesses, banks or government facilities. While normal forms of email filtering prove somewhat useful in the fight against phishing, the techniques are highly unlikely to catch the modern-day complex phishing channels such as zero-day phishing or spear phishing. As a result of this, there is a growing uptake of applying Artificial Intelligence (AI) and Machine Learning (ML) to address email security. They are capable of training on sampled volumes of emails and then using this training to improve the recognition of phishing and non-phishing instances. Here, we propose to work on a system of phishing detection using AI/ ML, which will be instrumentally crucial in making email security reliable and adaptable. The system employs Random Forest, Support Vector Machines (SVM) as well as Neural Networks to classify the emails based on the features extracted from the subject, body as well as links of the emails. By employing both phishing and innocuous email corpus, we trained and tested these models for the purpose of understanding the viability of phishing identification. The achievements of the study were the improved accuracy of detection when compared to conventional approaches and the further reduction of misrecognition, which improves security in general. It should be noted that by integrating a multi-model approach with learning mechanisms, the proposed system is indeed versatile and strong against advanced phishing threats.