Bone tumors represent a diagnostically challenging aspect of musculoskeletal radiology due to their rarity, histological diversity, and overlapping imaging features with various benign lesions and non-neoplastic conditions. Despite the multimodality approach utilizing radiographs, CT, MRI, and hybrid imaging modalities, even expert radiologists are often doubtful about the exact neoplastic etiology. The interpretation is complicated by morphological heterogeneity, technical acquisition variability, and limited exposure to rare tumor types in general practice. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offers the ability to augment bone tumor diagnosis and management through automated detection, classification, segmentation, and prognostication. Recent papers have demonstrated their capability to identify subtle imaging cues, integrate multimodal data, and deliver quantitative, reproducible analyses that support clinical decision-making. However, the translation of AI into routine bone tumor diagnostic workflow faces unique obstacles, including scarce and imbalanced datasets, benign-malignant mimicry, limited external validation, and various regulatory and ethical considerations. The otherwise common problem of “black box” nature of many DL systems presents additional challenges to clinician trust, underscoring the need for explainable AI solutions that highlight key imaging features influencing predictions. This chapter provides an in-depth overview of current AI applications in bone tumor imaging, appraising their performance and limitations. Finally, a forward-looking road map is presented—our perspective on the future ahead, emphasizing the development of subtype-aware models, robust multi-institutional validation, workflow-friendly integration into tumor care pathways, and transparent decision-support mechanisms.