This paper proposes a deep ensemble classifier with transfer learning for enhanced content-based fashion recommendation. The system uses five pre-trained models (MobileNet, DenseNet, Xception, and two VGG variants) to obtain probabilities, which are then passed to a deep ensemble classifier for prediction. Cosine similarity is used to recommend products based on the classification results. The proposed method is tested on benchmark datasets such as Fashion product images and Shoe datasets, achieving 96% accuracy, which is higher than existing models. The study highlights the potential of transfer learning and deep ensemble techniques in improving fashion recommendation systems. The system is designed to classify new items based on multiple models and retrieve similar images within the same product category. The main contributions include a novel deep ensemble classifier, the use of transfer learning, and the evaluation of the system on benchmark datasets. The results demonstrate the effectiveness of the proposed method in enhancing fashion recommendation accuracy. The system is evaluated using classification metrics and similarity measures, showing that it outperforms existing approaches in terms of accuracy and performance. The study also discusses the application of the proposed method in real-world scenarios, emphasizing its practicality and potential utility in fashion recommendation systems. The results indicate that the deep ensemble classifier effectively captures complex patterns in fashion images, leading to more accurate and reliable classification. The study contributes to the growing body of research in computer vision and fashion recommendation systems, demonstrating the effectiveness of the proposed method in tackling the challenges of fashion image classification and recommendation. Future research directions may include exploring additional candidate models and evaluating the proposed approach on more extensive and diverse datasets to validate its effectiveness and generalizability.This paper proposes a deep ensemble classifier with transfer learning for enhanced content-based fashion recommendation. The system uses five pre-trained models (MobileNet, DenseNet, Xception, and two VGG variants) to obtain probabilities, which are then passed to a deep ensemble classifier for prediction. Cosine similarity is used to recommend products based on the classification results. The proposed method is tested on benchmark datasets such as Fashion product images and Shoe datasets, achieving 96% accuracy, which is higher than existing models. The study highlights the potential of transfer learning and deep ensemble techniques in improving fashion recommendation systems. The system is designed to classify new items based on multiple models and retrieve similar images within the same product category. The main contributions include a novel deep ensemble classifier, the use of transfer learning, and the evaluation of the system on benchmark datasets. The results demonstrate the effectiveness of the proposed method in enhancing fashion recommendation accuracy. The system is evaluated using classification metrics and similarity measures, showing that it outperforms existing approaches in terms of accuracy and performance. The study also discusses the application of the proposed method in real-world scenarios, emphasizing its practicality and potential utility in fashion recommendation systems. The results indicate that the deep ensemble classifier effectively captures complex patterns in fashion images, leading to more accurate and reliable classification. The study contributes to the growing body of research in computer vision and fashion recommendation systems, demonstrating the effectiveness of the proposed method in tackling the challenges of fashion image classification and recommendation. Future research directions may include exploring additional candidate models and evaluating the proposed approach on more extensive and diverse datasets to validate its effectiveness and generalizability.