Numerous domains, including engineering, machine learning, and industrial processes, depend heavily on optimization. Early convergence or stagnation are frequently the results of traditional metaheuristic algorithms' incapacity to strike a balance between exploration and exploitation. To overcome these issues, this study suggests a novel hybridization of the Ant Lion Optimizer (ALO) and the Sine Cosine Algorithm (SCA). By combining the robust local search and trapping mechanisms of ALO with the quick exploration and straightforward structure of SCA, the hybrid model enhances overall optimization performance. The proposed hybrid SCA-ALO algorithm is evaluated on real optimization problems and 23 benchmark functions. In terms of convergence speed, solution accuracy, and robustness, comparative analysis shows that the hybrid continuously performs better than the standalone SCA and ALO. The hybrid model performs noticeably better in 15 of the 23 benchmark functions. The superiority of the hybrid approach is further supported by statistical analyses, such as mean fitness values and standard deviation. Its practical versatility is further demonstrated by real-world applications like feature selection, engineering design, and economical load dispatch. The results point to a strong framework for resolving dynamic and complex optimization problems through the hybridizationofcomplementary metaheuristic strategies, paving the way for framework for resolving dynamic and complex optimization problems through the hybridizationofcomplementary metaheuristic strategies, paving the way for further developments in the study of hybrid algorithms.