Ewing sarcoma (ES) is a rare but aggressive cancer, with survival rates of only about 30% in metastatic cases. Its rarity and severity highlight the need for focused research, yet progress is limited by scarce patient samples, few animal models, and minimal preclinical testing platforms. Conventional 2D cell cultures often fail to replicate primary tumor gene expression and drug sensitivity, while patient-derived xenografts (PDXs), though more accurate, require extensive resources and tend to lose tumor-specific traits when cultured in 2D. We established and standardized a spheroid culture system for ES and developed a semi-automated drug-screening platform for tumor spheroids. This platform enables drug testing in a 3D environment, offering more relevant insights than 2D models and helping to define drug concentration ranges for testing on tumoroids and patient samples. Bulk RNA sequencing revealed significant transcriptional differences between 3D cell line models (including spheroids and encapsulated 3D constructs) and traditional 2D cultures. The 3D constructs were created using extrusion bioprinting, which ensures uniform cell distribution and high density. Notably, the mevalonate pathway was activated in all 3D models used. ES is among the tumors with the highest expression of mevalonate pathway genes, making its activation in 3D models a promising replication of in vivo tumor characteristics. This activation underscores the pathway’s importance in ES metabolism and its potential as a therapeutic target. Building on these findings, we validated the encapsulated 3D models using PDX-derived cells, confirming consistent mevalonate pathway upregulation across these constructs. These models further demonstrated the spheroid platform’s capacity to accurately predict effective drug concentration ranges, which proved optimal for testing PDX-derived cells and may extend to other primary materials. Next, we conducted synergy tests using concentration matrices in both spheroid and 2D cultures, systematically exploring different drug concentration combinations. Based on transcriptional data and earlier studies, we opted to test interactions between mevalonate pathway inhibitors (statins), and apoptosis inhibitors, specifically BCL2 family inhibitors. Remarkably, we observed a strong synergy between BCL-xL inhibitors and statins, but only within the physiologically relevant 3D environments. These findings underscore the value of 3D models as potent tools for uncovering therapeutic combinations missed by conventional approaches. By more accurately reflecting key aspects of tumor behavior, these models enhance the reliability of drug response predictions and enable targeted treatment strategies. Advanced 3D models could accelerate the translation of preclinical discoveries into clinical treatments for Ewing sarcoma, offering new possibilities for tackling this aggressive cancer.