In this article, we study a new extension of the log-logistic model called the Kumaraswamy alpha-power log-logistic (KAPLL) distribution, an extension of the log-logistic model. It investigates some of their mathematical and statistical properties, including reliability properties (survival function, hazard rate function (HRF), moments, quantile functions (QF), and moment-generating functions), emphasizing their utility in modeling diverse aging and failure criteria. One key advantage of the KAPLL distribution lies in its capacity to represent its density as a blend of log-logistic densities, offering both symmetric and asymmetric shapes for greater modeling flexibility. The estimation of KAPLL parameters is achieved through maximum likelihood estimation (MLE), a widely used statistical method. The study presents comprehensive simulation results to assess the effectiveness of the proposed estimation technique. Furthermore, a practical application on real-world data is conducted to showcase the adaptability and versatility of the KAPLL distribution when compared with other extensions of the log-logistic model.