<p>This paper presents a morphological analyzer, which is a computational tool designed to automatically identify and analyze the morphological structure of words. This study focuses on the morphological analysis of nouns in the Dogri language using a paradigm approach to identify the morphological properties of words, such as base form, inflectional endings, and suffixes. The paradigm approach involves analyzing word forms within a given inflectional system, aiming to identify the patterns underlying their formation. The study also draws comparisons with inflectional patterns in related languages such as Hindi and Punjabi and identifies the morphological processes involved in forming various noun forms in Dogri. The model was evaluated on datasets of different sizes, yielding an F1-score of 46.20% and 49.01% on a 13,000-word dataset using hybrid and deep learning stemming techniques, respectively. On a larger dataset of 29,000 words, the F1-scores improved to 80.81% with the hybrid approach and 85.67% with the deep learning technique. During the current phase of research, with the expansion of the dataset to 59,652 words and the development of 32 paradigms, the system was further enhanced using a hybrid stemming technique<b>,</b> resulting in a significantly improved accuracy of 90.12%<b>.</b> These findings underscore that the paradigmatic approach, when combined with scalable data and hybrid methods, provides a robust framework for analyzing noun morphology and facilitates a deeper understanding of the patterns and processes involved in Dogri word formation.</p>