Land Use/Land Cover (LULC) change detection is fundamental for understanding landscape dynamics and supporting sustainable environmental management, particularly in ecologically sensitive and transboundary conservation areas such as Kruger National Park, South Africa. Rapid urban expansion, agricultural intensification, and environmental change have accelerated landscape transformation, highlighting the need for accurate and reliable LULC monitoring. This study aimed to evaluate long-term LULC dynamics in and around Kruger National Park during 2005, 2015, and 2025 using multi-temporal Landsat imagery and four machine learning classifiers: Random Forest (RF), Support Vector Machine (SVM), Gradient Tree Boosting (GTB), and K-Nearest Neighbors (KNN). In addition, RF-based transition matrices were employed to quantify long-term land cover transformations. The results showed that the Random Forest classifier consistently outperformed the other algorithms, achieving the highest Overall Accuracy (up to 92%) and Kappa coefficient (0.87) across all study years. Gradient Tree Boosting and K-Nearest Neighbors produced satisfactory results, whereas Support Vector Machine exhibited comparatively lower classification performance. Spatial analysis indicated that woodland/shrubland remained the dominant land cover class but experienced progressive fragmentation and conversion to grassland, agricultural land, and bare land. Built- up areas expanded substantially, particularly between 2005 and 2015, while agricultural land also increased, reflecting growing anthropogenic pressure. Water bodies exhibited a declining trend throughout the study period, suggesting increasing hydrological and environmental stress. These findings demonstrate that ensemble-based machine learning methods, particularly Random Forest, provide a robust and reliable approach for monitoring complex and heterogeneous savanna landscapes. The observed LULC changes highlight the increasing influence of human activities on ecosystem structure and emphasize the importance of continuous geospatial monitoring to support biodiversity conservation, sustainable land management, and evidence-based environmental planning. Overall, this study provides a reliable framework for long-term LULC mapping and change detection that can be applied to protected areas and other ecologically sensitive landscapes.