In today's digital landscape, fortifying cyber security is of utmost importance. This research introduces an innovative strategy that relies on transfer learning within deep neural networks to combat evolving threats, with a specific focus on phishing URLs and Malicious links, a major vector for cyber-attacks. We meticulously curate a diverse dataset of phishing and legitimate URLs, subjecting it to rigorous pre-processing. Departing from traditional methods, we leverage transfer learning to extract intricate patterns within URLs and their content. Our unique approach integrates transfer learning into a hybrid model, combining deep learning techniques with the power of transfer learning. This hybrid model employs soft and hard voting to optimize phishing threat detection accuracy and efficiency. We fine-tune our models with advanced feature selection and hyper parameter optimization, using rigorous evaluation metrics to assess performance