The rapid growth of networks has significantly impacted people, corporations, and governments. However, this expansion has also been accompanied by a sharp rise in cybercrime events, underscoring the necessity of putting strong security measures in place to protect electronic data. Among the various techniques used, Phishing is the leading approach for unlawfully obtaining critical information from online users. This deceptive practice commonly involves fraudulent attempts through emails and counterfeit websites. Phishers employ diverse tactics and strategies, enabling them to execute sophisticated phishing attacks, where attackers mimic official websites to collect personal information from online users. The objective of our study to analyze the effectiveness of fully connected networks and convolutional neural networks in phishing detection and improve reliability by reducing false positives and negative analyses, which can help cyber security specialists detect whether a URL is phishing or normal. We began by collecting data from EuRepoC. Subsequently, the gathered data undergoes various stages, including cleaning and preparation. Following this, we implemented an embedding layer to embed words and characters into low-dimensional vectors in both models. Next, we constructed fully connected networks (FCNs), Long Short Term Memory (LSTMs) and convolutional neural network (1D-CNNs) models. The subsequent steps involve training each model and evaluating their respective results. The data analysis reveals that fully connected networks (FCNs) achieve an 82% accuracy and 86% of long short term memory (LSTMs) in identifying phishing URLs, whereas convolutional neural networks VII demonstrate a higher accuracy of 98%. Regarding precision, fully connected networks perform at 82%, with a 96% recall and an 88% f1-score and long short term memory perform 99% precision, with an 81 % recall and an 89 f1-score.Conversely, convolutional neural networks exhibit a precision of 99% and maintain a high 98% for f1-score and 100 % recall. Examining false positives and false negatives, fully connected networks exhibit a 4 %( FN), and 55 %( FP) occurrence, while long short term memory show 19 % (FN) , and 2 % (FP).Finally, convolutional neural networks show minimal instances with 11 % false positives and 0 % false negatives. Additionally, the area under ROC curve indicates a 71% performance for FCN, a 90 % LSTM, and 98% for 1D-CNN.