Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3928 2018 2018 Department Computer Science, College of Computer Science and Information Technology, University of Kerbala, Kerbala, Iraq Heba Heba Department Artificial Intelligence Engineering, College of Information Technology Engineering, Al-Zahraa University for Women, Kerbala, Iraq Hiba Jabbar Aleqabie Presidency of the University of Babylon, University of Babylon TOEFL Center, Babylon, Iraq Ameer Sameer Hamood Mohammed Ali The task of automatically generating descriptive and accurate image tags has gained significant attention in recent years due to the exponential growth of image data. Traditional methods for image tagging rely on manual annotation, which is time-consuming and subjective. Automated imagine description fills the gap between visual content and human comprehension, making it vital for activities such as information retrieval, editing, and accessibility. The expanding number of unannotated photographs makes manual tagging impossible. This paper provides a deep learning-based system that combines CNNs for feature extraction, RNNs for caption production, and attention techniques to focus on significant image areas. The model uses a sequence-to-sequence architecture to create coherent captions using pre-trained CNN features and attention-enhanced RNNs. Experiments on datasets such as Flickr8k and Flickr30k show higher performance, as evidenced by BLEU, ROUGE, and CIDEr measures. This approach provides a scalable, cutting-edge solution for image captioning, with potential applications in video analysis, enriched language production, and larger datasets.   2026 2026 165 175 10.54216/FPA.210112 https://www.americaspg.com/articleinfo/3/show/3928