Named Entity Recognition (NER) is an essential component of natural language processing. Marathi is an under-resourced language used by significant worldwide users specially in Maharashtra. The primary objective of this study is to enhance NER development in Marathi news articles by applying the machine learning algorithm Hidden Markov Model (HMM) with a pattern matching approach. The NER system trains HMM on a manually annotated news corpus using the Viterbi decoding algorithm to assign the most probable named entity tags to words in the test dataset. The baseline NER system achieves a 63.6% named entity identification and a 72.95% classification accuracy. The NER system's identification and classification rate is improved to 82.07% and 86.84%, respectively, by integrating pattern matching techniques. This study sets a benchmark for Marathi NER development vitally used for natural language processing tasks such as machine translation, question answering, summarization, and information retrieval systems. The study presented in this paper is an important basic component that uses robust methodology, a detailed dataset preparation method, and comprehensive related research, ensuring the reliability and validity of the named entity recognition results.