<![CDATA[The rapid development of maritime transportation, autonomous navigation, and digital surveillance has increased the need for reliable Maritime Situational Awareness (MSA) based on complementary sensing sources. This study systematically reviews multi-sensor integration and data-fusion research for maritime situational awareness and traffic surveillance, with emphasis on sensor/data sources, fusion approaches, operational applications, benefits, limitations, and unresolved research gaps. A Systematic Literature Review (SLR) was conducted using Scopus-indexed literature published between 1971 and 2026. The selection process followed PRISMA 2020, resulting in 82 English-language journal articles for final analysis. The evidence was synthesized through article-level thematic coding, while VOSviewer was used to examine keyword co-occurrence, research clusters, and temporal development. The findings show that AIS, radar, camera/visual sensors, satellite/SAR, and environmental data form the principal information sources, while data/sensor fusion, artificial intelligence and deep learning, Kalman-based tracking, feature-level fusion, graph/association methods, fuzzy logic, and Bayesian approaches represent the major methodological families. Applications are concentrated in MSA, maritime surveillance, vessel tracking, autonomous navigation, vessel detection and recognition, and collision-risk support. The review further identifies persistent challenges involving heterogeneous data, temporal-spatial synchronization, environmental uncertainty, computational requirements, cybersecurity, missing observations, and operational scalability. The study contributes an integrative evidence structure connecting sensing sources, fusion methods, operational applications, performance benefits, implementation constraints, and future research priorities for maritime surveillance and Vessel Traffic Services (VTS).]]>
