Introduction: Medical tourism has emerged as one of the fastest-growing sectors in the global tourism industry, driven by factors such as rising healthcare costs in developed countries, advancements in medical technologies, and the proliferation of high-quality treatment centers worldwide. According to Grand View Research, the market is projected to exceed $208 billion by 2027, underscoring the need for innovative marketing strategies amid intensifying competition. Personalized marketing, leveraging data and digital technologies like AI, big data, and recommender systems, plays a pivotal role in attracting international patients and enhancing their satisfaction by tailoring services to individual needs. Despite substantial research growth, the literature remains fragmented, geographically limited, and lacks systematic analysis of personalization's evolution from traditional to data-driven models. This study conducts a qualitative systematic review (2005–2025) to identify trends, concepts, and gaps, proposing a conceptual framework integrating smart technologies, patient behavior, strategies, and ethics. Key research questions address temporal/geographical trends, theoretical models, thematic areas, technology's role, gaps, and a global personalization framework in medical tourism. Methods: This study adopted a qualitative systematic review design guided by the PRISMA framework to ensure methodological transparency and replicability. The review process was further supported by the Nested Knowledge platform to facilitate structured synthesis and data management. A comprehensive search was conducted in the Scopus database covering the period from 2005 to 2025. The search strategy incorporated combinations of keywords such as “personalized marketing,” “medical tourism,” “artificial intelligence,” and “recommender systems.” The initial search yielded 2660 records. After applying predefined inclusion and exclusion criteria, including limiting the selection to peer-reviewed journal articles published in English and ensuring direct relevance to personalization within the context of medical tourism, 273 empirical studies were retained for final analysis. Non-empirical publications, conference abstracts, and studies lacking substantive relevance were excluded. Data extraction and classification were structured using the PICO framework: Population (digital users and international patients), Intervention (personalized strategies and intelligent technologies), Comparison (traditional versus technology-enabled marketing approaches), and Outcome (patient satisfaction, loyalty, and related performance indicators). The analytical process involved thematic coding, chronological trend mapping, and systematic gap identification. This multi-step procedure enhanced analytical rigor and ensured the transparency and reproducibility of the review process. Finding: The temporal analysis indicates that more than 80% of the identified studies were published after 2020, reflecting the rapid maturation and widespread adoption of artificial intelligence technologies. Geographically, the body of research is predominantly concentrated in developed countries,particularly the United States and European nations,while contributions from emerging economies remain limited. Thematic clustering of the literature reveals three dominant streams: (1) digital consumer behavior, including the use of predictive algorithms; (2) technological applications, particularly AI-driven personalization and recommender systems; and (3) outcome-oriented studies focusing on enhanced patient satisfaction and loyalty. Prominent conceptual and analytical models include predictive marketing based on machine learning and AI-enabled content personalization. Despite these advancements, several critical gaps persist. Empirical validation of proposed models remains insufficient, and limited attention has been paid to ethical considerations, data privacy concerns, and institutional-level applications within the medical tourism sector. Although technologies such as artificial intelligence and big data analytics facilitate behavioral prediction and targeted advertising, underexplored areas include the protection of international patient data and cross-cultural ethical implications. Conclusions: This review highlights the uneven growth of personalized marketing research in medical tourism, emphasizing technology's potential while identifying gaps in empirical evidence, geographical diversity, and ethical frameworks. The proposed conceptual model links smart technologies, patient preferences, marketing strategies, and ethics to outcomes like loyalty and experience. Future research should prioritize field studies, emerging markets, and policy development for data privacy. Ultimately, balanced adoption of personalization can sustain medical tourism's competitiveness.