Personalized marketing campaigns have become a cornerstone of modern business strategies, enabling companies to engage customers more effectively and drive higher conversion rates. However, traditional marketing approaches often fail to address individual customer preferences at scale, leading to suboptimal results. Artificial Intelligence (AI) and Deep Learning (DL) have emerged as transformative tools for creating highly personalized marketing campaigns by analyzing customer data, predicting behavior, and generating tailored content. This paper explores the current landscape of AI-powered personalized marketing, focusing on key techniques such as customer segmentation, natural language processing (NLP), and predictive analytics. Despite significant advancements, challenges such as scalability, real-time processing, and integration with compact devices remain. This study identifies research gaps and proposes innovative solutions, including lightweight AI models, advanced data augmentation techniques, and real-time analytics integration. By addressing these challenges, businesses can enhance customer engagement, optimize marketing ROI, and deliver more impactful campaigns. The findings underscore the potential of AI to revolutionize personalized marketing and provide actionable insights for future research and implementation

AI, Personalized Marketing, Customer Segmentation, NLP, Predictive Analytics, Deep Learning.

AI-powered personalized marketing has emerged as a transformative approach for businesses to engage customers, drive sales, and optimize marketing ROI. By leveraging advanced techniques such as customer segmentation, natural language processing (NLP), and predictive analytics, businesses can deliver highly tailored campaigns that resonate with individual preferences. However, challenges such as scalability, real-time processing, and integration with compact devices remain significant barriers to widespread adoption. This study highlights the potential of AI to revolutionize personalized marketing while identifying key research gaps, including the need for lightweight models, standardized datasets, and ethical frameworks. Addressing these challenges through innovative solutions—such as edge computing, data augmentation, and cross-industry collaboration—will pave the way for more efficient and impactful marketing strategies. The findings underscore the importance of continuous innovation in AI to enhance customer engagement and campaign effectiveness. By embracing these advancements, businesses can unlock the full potential of personalized marketing, ensuring a competitive edge in the digital era. Future research should focus on developing scalable, ethical, and real-time AI solutions to further bridge the gap between technology and marketing success.

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