<p>Sodium alginate has emerged as a cornerstone biopolymer for encapsulation, offering biocompatibility, tunable gelation properties, and widespread applications across biomedical, food, and environmental sectors. This review traces the historical evolution of alginate encapsulation, from its early adoption to its current advancements, and explores the physicochemical mechanisms underlying its encapsulation efficiency. Key aspects such as stability, controlled release, and interactions with crosslinking ions are examined, alongside regulatory considerations and global safety standards. This review specifically highlights the synergy that arises from the use of predictive modeling through AI on the one hand, and the concept of socio-economic sustainability on the other; a nexus, which is not commonly covered in today’s literature. Using architectures within the realm of Machine Learning algorithms, namely Artificial Neural Networks (ANN) and XGBoost, it is possible to accelerate rheological analysis by a factor of 70 and achieve encapsulation efficiencies exceeding 90%, thereby translating alginate research from empirical trial-and-error methodologies to data-driven, predictive, and digitally optimized design frameworks. The review also addresses the socio-economic and environmental implications of alginate encapsulation, emphasizing its role in sustainability and waste reduction. Finally, we discuss future perspectives, highlighting both the opportunities and challenges in commercializing alginate-based encapsulated products. By bridging fundamental knowledge with cutting-edge advancements, this review provides a comprehensive outlook on the evolving landscape of alginate encapsulation and its transformative potential in multiple industries.</p>