Information extraction (IE) is the process of extracting relevant and useful patterns or information from unstructured data. Named entity recognition (NER) is a subtask of IE that identifies entities from unstructured text documents and organize them into different predefined categories such as person, location, organization, number, date, etc. NER is considered to be one of the important steps in natural language processing which may find direct applications in areas such as question answering (QA), entity linking, and co-reference resolution, to name a few. NER systems perform comparatively well in high-resource languages such as English but there is a lack of well-developed NER systems for low-resource languages such as Malayalam, which is an Indic language spoken in the state of Kerala, India. This work is an approach in this direction which makes use of deep learning (DL) techniques for developing a NER system for Malayalam. We have compared different DL approaches such as recurrent neural networks, gated recurrent unit, long short-term memory, and bi-directional long short-term memory and found that DL based approaches significantly outperform traditional shallow-learning based -approaches for NER. When compared with some state-of-the-art approaches our proposed framework is found to be outperforming in terms of precision, recall, and F-measure and could achieve an improved precision of 7.89% and 8.92% of F-measure.