Mountaineering is an adventurous sport that, alongside its appeal, involves numerous risks. The world’s high mountains have always been challenging destinations for climbers, and ascents are often accompanied by serious dangers. Many mountaineers have lost their lives while attempting to climb these peaks; nevertheless, these mountains especially those above 8,000 meters remain yearly targets for climbers worldwide. This study aims to analyze the patterns of fatal incidents on the world’s 14 highest peaks and to propose strategies for reducing casualties, using data mining methods such as clustering, survival analysis, and logistic regression. The data, obtained from the Kaggle repository, include information such as mountain name, date of incident, cause of death, and climbers’ nationality. Findings indicate that Mount Everest, with 35% of the fatalities, is the most dangerous mountain in the world. Avalanches (over 40%), falls (about 30%), and various altitude-related illnesses were identified as the main causes of death. Spring, with 55% of accidents, was found to be the most hazardous season due to increased climbing traffic. The logistic regression predictive model, with 87% accuracy, showed that altitudes above 8,500 meters and the spring season have the greatest impact on the occurrence of fatal incidents. The study suggests that measures such as limiting climbing capacity, installing avalanche warning systems, and using health monitoring equipment could reduce accident risks. The results can contribute to safer expedition planning and the development of preventive policies.