Outlier detection is essential for identifying irregularities in data sets in different fields, including cybersecurity, fraud detection, and medical diagnostics. In literature, outlier identification is done using traditional machine learning methods, however these methods show low performance when dealing with complex datasets or high-dimensional data. This study examines neural network techniques for outlier identification, focusing on Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Gated Recurrent Units (GRU) in detecting outlier with complex or high-dimensional dataset. We evaluate the efficacy of these models using distinct datasets referred to as KDD99, ALOI, and Ann-Thyroid. Performance is assessed based on accuracy, precision, recall and F1-score to provide a holistic comparison. Experimental results show that CNNs consistently perform better than the other models, especially when dealing with datasets that have complex patterns. These results highlight the potential of neural network particularly CNNs as a strong and reliable approach for detecting outliers, even in challenging, high-dimensional data.