In this paper, we showcase a comprehensive end-to-end pipeline for creating a superior Bartangi language corpus and using it for training word embeddings. The critically low-resource Pamiri lan- guage of Bartangi, which is spoken in Tajikistan, has difficulties such as morphological complexity, orthographic variety, and a lack of data. In order to overcome these obstacles, we gathered a raw corpus of roughly 6,550 phrases, used the Uniparser-Morph-Bartangi morphological analyzer for linguistically accurate lemmatization, and implemented a thorough cleaning procedure to eliminate noise and ensure proper tokenization. The lemmatized corpus that results greatly lowers word spar- sity and raises the standard of linguistic analysis.The processed corpus was then used to train two different Word2Vec models, Skip-gram and CBOW, with a vector size of 100, a context window of 5, and a minimum frequency threshold of 1. The resultant word embeddings were displayed using dimensionality reduction techniques like PCA and t-SNE, and assessed using intrinsic methods like nearest-neighbor similarity tests. Our tests show that even from tiny datasets, meaningful semantic representations can be obtained by combining informed morphological analysis with clean prepro- cessing. One of the earliest computational datasets for Bartangi, this resource serves as a vital basis for upcoming NLP tasks, such as language modeling, semantic analysis, and low-resource machine translation. To promote more research in Pamiri and other under-represented languages, we make the corpus, lemmatizer pipeline, and trained embeddings publicly available.