Understanding wind patterns, particularly in regions like Algeria with diverse climates, is crucial. Conventional models like the two-parameter Weibull distribution may not suffice, especially in areas exhibiting complex wind behaviors. The bimodal mixture Weibull distribution was suggested to model the stochastic variations of wind speed at In-Salah, Algeria’s windiest site. Various estimation techniques were applied to evaluate the parameters of the Weibull distributions. Hourly wind speed data measured at 10 m AGL for ten years were deployed. Despite the flexibility offered by mixture distributions, their numerous parameters impose challenges in the estimation process. To address this, a grid search approach was utilized to optimize parameter selection, though being computationally expensive. Statistical analysis across overall, yearly, monthly, and hourly samples demonstrated the superior performance of the mixture Weibull distribution, particularly when equipped with the weighted least squares estimation method. The findings highlight the effectiveness of the weighted least squares method in providing robust estimates, leading to accurate assessment of the wind power potential in the region.