Pediatric solid tumors are among significant causes of morbidity and mortality in children, for successful treatment outcomes of which early detection is crucial. However, accurate diagnosis can be challenging due to the complexity and variability of these tumors. Digital pathology, which involves the digitization of traditional glass slides and the use of computer algorithms for analysis, has emerged as a promising tool for improving diagnostic accuracy and efficiency. Furthermore, artificial intelligence (AI) technologies, such as machine learning and deep learning algorithms, have shown great potential in analyzing large volumes of histopathological data to identify patterns and features that may not be readily apparent to human observers. By utilizing the power of digital pathology and AI, healthcare providers can potentially achieve earlier and more accurate diagnoses of pediatric solid tumors, leading to improved patient outcomes. The implications of implementing digital pathology and AI in pediatric oncology include the development of personalized treatment plans, improved prognostic accuracy, and enhanced research capabilities. Additionally, these technologies have the potential to streamline workflows, reduce diagnostic errors, and optimize resource allocation within healthcare systems. Ultimately, the integration of digital pathology and AI has the potential to revolutionize the approach to pediatric solid tumors diagnosis and management, paving the way for more effective healthcare strategies and improved outcomes for pediatric cancer patients. This chapter explores the potential of utilizing digital pathology and AI for the early diagnosis of pediatric solid tumors and its implications for improved healthcare strategies.