This study sets out a data-driven Political Recommendation Framework that strategically matches candidate selection to local demographic realities to bolster equitable representation and electoral strategy. The goal is to contribute a generally applicable approach capable of helping political party decision-makers make strategic, inclusive, and evidence-based nomination selections, based on a test case of Varanasi, Uttar Pradesh. The framework employs a K-means clustering strategy to categorize administrative zones into high- and low-prioritized segments based on demographic factors (i.e., gendered ratio, caste profile [General, SC/ST, OBC], household count, slum population, literacy, and working population). The main inputs are a) the Census of India 2011; and b) deconstructed local electoral data (e.g. Election Commission of India) that were used to prepare the local mapping framework. The overall methodology includes documentation of cleaning, normalizing, and validating processes by domain experts to account for political objectives and context; the data analyses found a concerning disjuncture between contextualized voter profile and candidate profile by demographic categories for Varanasi. Women appear to have more than 43% of the voter pop in many zones, but representation of women candidates has been reported at below 15% in candidate fields in Varanasi. The caste imbalances are pronounced, especially when there is either an over-concentration or no SC/ST and OBC candidates in significant areas. Focusing the nominations of candidates according to demographic clusters should help in political resonance, voter activation, and legitimacy in the electoral process. Political parties can examine the model to improve alignment of candidates selected with constituency specific demographic needs. The model would also support targeting campaigning, making evaluations of candidates a clear process for voters, and optimally using resources to attend to emerging zones of significance with previously unreached percentages of underrepresented groups. The study contributes to the nascent field demonstrating the use of an unsupervised learning approach to Indian electoral data to recommend candidates, while showing that, through data analytics of demography and aspects of political strategy, new paths around evidence-based electoral decision-making may be opened in democratic regimes.