Anomaly detection acts significant role in various domains, including cyber security, fraud detection, and industrial monitoring. Traditional approaches depend on handcrafted features and assumptions about data distributions, limiting their effectiveness and adaptability. This paper introduces Deviation Networks, a deep learning-based technique useful for abnormality identification that leverages the power of deep neural networks to learn complex representations directly from data. Deviation Networks employ an encoder-decoder architecture combined with deviation-based loss functions to capture normal patterns in the training data and identify deviations indicative of anomalies. This paper provides an in-depth exploration of Deviation Networks, including their architecture, training procedure, and evaluation metrics. Furthermore, we present experimental results on benchmark datasets, demonstrating the effectiveness and superiority of Deviation Networks compared to traditional anomaly detection methods. We also discuss practical considerations, such as data pre-processing, hyper parameter tuning, and deployment strategies. Overall, this paper showcases the potential of identification of abnormalities using Dense Networks and highlights their significance in addressing real-world anomaly detection challenges.