###### Abstract  
Phishing emails remain a primary vector for cyberattacks, necessitating advanced detection mechanisms. Existing studies often focus on limited datasets or a small number of models, lacking a comprehensive evaluation approach. This study develops a novel framework for implementing and testing phishing email detection models to address this gap. A total of fourteen machine learning (ML) and deep learning (DL) models are evaluated across ten datasets, including nine publicly available datasets and a merged dataset created for this study. The evaluation is conducted using multiple performance metrics to ensure a comprehensive comparison. Experimental results demonstrate that DL models consistently outperform their ML counterparts in both accuracy and robustness. Notably, transformer-based models BERT and RoBERTa achieve the highest detection accuracies of 98.99% and 99.08%, respectively, on the balanced merged dataset, outperforming traditional ML approaches by an average margin of 4.7%. These findings highlight the superiority of DL in phishing detection and emphasize the potential of AI-driven solutions in strengthening email security systems. This study provides a benchmark for future research and sets the stage for advancements in cybersecurity innovation. p 2025 202  
threats in real time [8]. Machine learning (ML) and deep learning (DL) have been extensively studied in the literature as optimal methods for the automated detection of phishing emails. ML techniques, such as naive Bayes [9], logistic regression [10], and random forest [11], have been widely utilized for their ability to classify emails based on handcrafted features like sender information, content structure, and metadata [12; 13; 14; 15; 16; 17]. These methods offer a relatively straightforward and computationally efficient approach to detecting phishing emails. On the other hand, DL models, including convolutional neural networks (CNNs) [18], recurrent neural networks (RNNs) [19], long short-term memory (LSTM) [20], and transformer-based architectures like BERT [21], are increasingly recognized for their ability to automatically extract complex features from raw email data [22; 23]. By leveraging advanced neural architectures, DL models can analyze email content's contextual and semantic nuances, enabling higher accuracy and robustness in phishing detection. The success of ML and DL models in other domains [24; 25] has fueled their application in phishing detection, establishing them as essential tools for enhancing email security. Despite extensive research on phishing detection, most studies are limited in scope, often focusing on a few datasets or a narrow range of models [12; 26]. This narrow focus restricts their applicability in real-world scenarios [27; 28], where phishing patterns vary significantly across datasets. Furthermore, DL models, which have shown exceptional promise in various domains, remain underexplored in phishing email detection [29]. Addressing these gaps is critical for developing robust and adaptive solutions to combat evolving phishing tactics effectively. Research Gap. While significant research has been conducted on phishing detection, several gaps remain:  
* Most prior studies rely on a single or a limited number of datasets, which restricts the generalizability of their findings to diverse phishing scenarios. * Despite the success of DL in other domains, its potential in phishing detection--especially using advanced architectures such as transformer-based models--has not been fully investigated. * Existing studies often lack a standardized framework for consistently implementing and evaluating ML and DL models across multiple datasets. Research Questions. This study addresses the following research questions:  
RQ1: What are the most effective ML and DL models for phishing email detection? RQ2: How does the performance of ML and DL models vary when applied to multiple individual datasets versus a merged dataset?