Person re-identification (ReID) has become an important task in digital surveillance for enhancing security, efficient monitoring, and enabling various applications in smart cities and public safety systems. Person ReID with attributes is a challenging task due to different camera views create significant difficulties in capturing each person’s unique identity and detailed attributes. In this work, we propose a multi-task model that not only performs unique person ReID but also simultaneously predicts attributes. Our model jointly utilizes a shared backbone network, which can be either ResNet50 or EfficientNet, along with generalized mean (GeM) pooling to achieve efficient feature extraction. It also applies attribute-specific heads to predict various characteristics such as gender, age, type of clothes, color, and alongside the ReID classification. This multi-task approach utilizes the shared features across tasks, gives comprehensive attribute predictions, and may further contribute to identification in surveillance scenarios. We evaluate our model on two commonly used publicly available datasets, Market1501 and DukeMTMC-reID, demonstrating how our approach can improve both in ReID accuracy and give reliable attribute predictions. These results reveal that our multi-task model can be competitive, providing a holistic solution for practical applications in surveillance where both identification and attributes are important. The approach has shown the potential of unifying ReID with attribute prediction to develop more robust and advanced surveillance systems. The code of this experiment is publicly accessible at https://github.com/TripleTheGreatDali/ReIDMTMASH.