Nowadays, with the exponential growth of digital connectivity and online services, phishing has emerged as one of the most familiar Cyber threats, targeting individuals and organizations alike. Phishing attacks aim to deceive users into revealing sensitive information such as usernames, passwords, and financial credentials by mimicking legitimate websites. To help mitigate these risks, there is an urgent need for intelligent systems capable of detecting and preventing phishing attacks in real-time. In this framework, various classification machine learning techniques are used, such as, Artificial neural network (ANN) achieved an accuracy of 98.90%, the Recurrent neural network (RNN), achieved an accuracy of 95.06%, and the K-Nearest Neighbors (KNN) achieved an accuracy of 97.30%, while the Convolutional neural network classifier predicted training error 99.80% and test error 99.13% with accuracy loss 0.005% and validation loss 0.03% between the evaluated models with false positive rate 0.0011. While CNN with stratified k-fold cross validation achieved an accuracy of 98.35%, and Inference Time per instance 0.000150 s was recorded. For model interpretability used Shapley additive explanations (SHAP) were used and analyzed false positive rate. The best model here is the CNN classifier on the URL-based phishing dataset with the highest accuracy of 99.80%.