This study considers the digital transformation of Kazakhstan’s agro-industrial complex, which has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. This study proposes a hybrid decision-support framework integrating a modified NSGA-III algorithm with machine learning techniques for optimizing digital marketing strategies in the agro-industrial complex of Kazakhstan. The model considers three objectives: maximizing channel efficiency and audience reach while minimizing marketing costs. Experimental results based on a dataset of N = 1200 observations demonstrate that the proposed approach improves the composite performance indicator by 12.4% compared to baseline single-objective optimization methods. Pareto front analysis reveals three distinct clusters of strategies, corresponding to (1) high-impact integrated digital TV strategies, (2) cost-efficient traditional channel strategies, and (3) high-risk high-return allocations. The clustering validity is confirmed by a silhouette score of 0.624, indicating strong separation between strategy groups. The results highlight the practical significance of adaptive budget allocation and demonstrate the effectiveness of combining evolutionary optimization with machine learning for decision support in complex marketing environments.

The article considers that the digital transformation of Kazakhstan's agro-industrial complex has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous consumer behavior. Existing approaches often rely on simplified analytical models that fail to capture the nonlinear and multi-criteria relationships inherent in agricultural marketing. This study addresses that gap by proposing a hybrid decision-support framework that integrates a modified NSGA-III evolutionary algorithm with machine learning techniques to optimize the allocation of marketing budgets across digital and traditional channels. The approach incorporates three key performance criteria—channel efficiency, target audience reach, and marketing cost—while embedding sector-specific constraints related to the AIC of Kazakhstan. A comparative analysis of current digital marketing optimization practices reveals that most methods lack adaptability to sector-specific production cycles, do not sufficiently analyze trade-offs, or fail to integrate machine learning for post-optimization interpretation. The results of the hybrid model show the formation of three distinct clusters of Pareto-optimal strategies, demonstrating the need for differentiated and adaptive budget allocation patterns. The outcomes confirm that the proposed hybrid methodology provides a rigorous scientific basis for designing cost-effective and high-impact marketing strategies within the digital transformation agenda, AIC.