<p>The need for flexible statistical models capable of capturing complex real-life phenomena has intensified across various fields where classical distributions often fail to provide adequate fit. Motivated by this requirement, we introduce a new three-paramter weighted probability model, termed the weighted quasi Garima distribution. The proposed distribution enhances modeling adaptability, particularly for positively skewed and heterogeneous data. We derive its fundamental statistical properties, including moments, moment generating and characteristic function, survival and hazard rate behaviours, order statistics, and entropy measures, thereby establishing its theoretical robustness. Parameter estimation is carried out using maximum likelihood method, and the performance of these estimators is examined through a comprehensive simulation study. A likelihood ratio test is employed to evaluate the comparative fit of the weighted quasi-Garima distribution against quasi Garima distribution. Finally, analysis of a real life data set demonstrates that the proposed model provides a superior fit relative to several competing distributions, highlighting its potential for practical applications.</p>