Stochastic models play a pivotal role in disease prediction by accounting for randomness and uncertainty in biological systems. This study offers a visualization of trends in the application of stochastic models for disease prediction from 1990 to 2024, based on a bibliometric analysis of Scopus data. Key findings reveal a significant growth in research post-2014, largely driven by global health challenges like COVID-19. Despite these advancements, gaps remain in applying these models to non-communicable diseases and low-resource settings. By integrating computational techniques like machine learning, stochastic models hold promise for improving predictive accuracy. This study highlights the need for further international collaboration and interdisciplinary research, offering practical insights for researchers and public health professionals aiming to enhance disease prediction and intervention strategies.