Part-of-speech (POS) tagging is a fundamental task in natural language processing (NLP) that involves assigning grammatical categories to words in a sentence. In this study, we investigate the application of deep sequential models for POS tagging of Telugu, a low-resource Dravidian language with rich morphology. We use the Universal dependencies dataset for this research and explore various deep learning architectures, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and their stacked variants for POS tagging. Additionally, we utilize multilingual BERT embeddings and indicBERT embeddings to capture contextual information from the input sequences. Our experiments demonstrate that stacked LSTM with multilingual BERT embeddings achieves the highest performance, outperforming other approaches and attaining an F1 score of 0.8812. These findings suggest that deep sequential models, particularly stacked LSTMs with multilingual BERT embeddings, are effective tools for POS tagging in Telugu.