This study presents the design and evaluation of a personalized recommender system aimed at predicting consumer behavior within digital marketing environments. The proposed model integrates the strengths of Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs) to effectively process and learn from sequential data. LSTM units are employed to capture long-range temporal dependencies in user interactions, while RNN layers provide a framework for processing dynamic sequences of consumer activity over time. The model is trained using a dataset derived from real-world digital marketing platforms, which includes detailed logs of user preferences, purchase history, and browsing patterns. By learning from these behavioral indicators, the hybrid LSTM-RNN model is able to generate highly personalized recommendations tailored to individual consumers. Experimental results indicate that the proposed architecture achieves a high level of predictive accuracy, outperforming traditional recommendation methods in several key performance metrics such as precision, recall, and F1-score. These findings underscore the effectiveness of deep learning approaches in modeling complex consumer behaviors and highlight the potential of neural network-based recommender systems in optimizing marketing campaigns and enhancing user engagement. Ultimately, this research contributes to the advancement of intelligent recommendation technologies in the digital marketing domain, offering practical implications for businesses aiming to deliver more targeted and responsive customer experiences.