Over the past three decades, the biodegradable polymer known as poly(propylene fumarate) (PPF) has been the subject of numerous research due to its unique properties. Its biocompatibility and controllable mechanical properties have encouraged numerous scientists to manufacture and produce a wide range of PPF-based materials for biomedical purposes. Additionally, the ability to tailor the degradation rate of the scaffold material to match the rate of new bone tissue formation is particularly relevant in bone tissue engineering, where synchronized degradation and tissue regeneration are critical for effective healing. This review thoroughly summarizes the advancements in different approaches for PPF and PPF-based composite scaffold preparation for bone tissue engineering. Additionally, the challenges faced by each approach, such as biocompatibility, degradation, mechanical features, and crosslinking, were emphasized, and the noteworthy benefits of the most pertinent synthesis strategies were highlighted. Furthermore, the synergistic outcome between tissue engineering and artificial intelligence (AI) was addressed, along with the advantages brought by the implication of machine learning (ML) as well as the revolutionary impact on regenerative medicines. Future advances in bone tissue engineering could be facilitated by the enormous potential for individualized and successful regenerative treatments that arise from the combination of tissue engineering and artificial intelligence. By assessing a patient’s reaction to a certain drug and choosing the best course of action depending on the patient’s genetic and clinical characteristics, AI can also assist in the treatment of illnesses. AI is also used in drug research and discovery, target identification, clinical trial design, and predicting the safety and effectiveness of novel medications. Still, there are ethical issues including data protection and the requirement for reliable data management systems. AI adoption in the healthcare sector is expensive, involving staff and facility investments as well as training healthcare professionals on its application.

Multiple myeloma (MM) and osteosarcoma (OS) are two biologically distinct osseous malignancies with similar molecular networks that present translational challenges for their computational modeling. This comparative research analyzes MM and OS biology relevant to in silico approaches, focusing on PI3K-AKT-mTOR signaling, the RANK-RANKL-OPG axis, angiogenic factors (VEGF, TGFs), and immune mediators in MM, alongside the transcription factors (SOX9, RUNX2), signaling pathways (PI3K-AKT-mTOR, NOTCH), immune cell state (TAM2), and interleukins in OS. Based on this pathophysiologic foundation, the review outlines five computational paradigms: (i) mechanistic models; (ii) data-driven/machine learning schemes; (iii) hybrid mechanistic approaches; (iv) digital twins/virtual cohorts, and (v) MIDD/PBPK models for real-world applications. A cross-cancer comparison section summarizes common and distinct biological axes and their computational translation as well as the overlapping features from the bone microenvironment. For both MM and OS, the research assesses strengths, limitations, and data needs of current models, outlining the strategic objectives for next-generation multiscale, AI-enabled models providing a roadmap for tissue engineers, oncology scientists, and translational researchers to design clinically relevant preclinical tests and accelerate safer, more effective strategies for tumor-affected bones. The differences between MM and OS impose distinct biological constraints, so their comparisons are rare. Combining all these features with artificial intelligence capabilities will underpin a promising transition in the development of in silico adaptive and learning models.