Phishing attacks pose a significant cybersecurity threat globally, with developing nations like Nigeria facing unique challenges due to localized tactics and cultural factors. This paper presents a novel approach to phishing mitigation in Nigeria, leveraging Natural Language Processing (NLP) and Deep Learning techniques to enhance both automated detection and user training. We analyze a corpus of Nigeria-specific phishing attempts, identifying linguistic patterns and cultural references commonly exploited by attackers. Using this data, we train a deep learning model capable of detecting localized phishing content with high accuracy. Building on this technical foundation, we design a dynamic anti-phishing training program that adapts to individual user behavior and local phishing trends. A Hybrid Deep learning models- recurrent neural networks (RNNs) and transformer-based models (BERT), was trained on large datasets of phishing and legitimate samples to learn discriminate features and classify new instances. Our results demonstrate significant improvements in both automated phishing detection rates and user resilience to social engineering tactics. The model achieved high precision (0.89), recall (0.94), and F1-scores (0.92, 1.00).This research contributes to the field by showcasing the potential of combining advanced AI techniques with culturally informed strategies to create more effective, localized cybersecurity solutions.