High-throughput screening has revolutionized material design, enabling the accelerated development and optimization ofmaterials through data-driven approaches. This study employs drop-on-demand inkjet printing to fabricate combinatorialsuperconducting 𝑅𝐸𝐵𝑎₂𝐶𝑢₃𝑂_7−𝛿 (REBCO, 𝑅𝐸 = 𝑌_𝑥𝐺𝑑_1−𝑥) thin films with a controlled compositional gradient, allowing asystematic investigation of Rare Earth composition effects on the Transient Liquid Assisted Growth (TLAG) process. Amethodological framework is established for fabricating and characterizing combinatorial REBCO thin films. Automated andsynchrotron-based techniques—including Interferometry, EDX, XRD, and Scanning Hall Probe Microscopy (SHPM)—are used tobuild comprehensive property maps, revealing composition-driven variations in crystal growth and superconducting properties.Furthermore, SEM and STEM confirm REBCO crystal quality and provide deeper material insights at specific combinatorialsample locations. This framework also lays the groundwork for integrating machine learning approaches into the understandingof the TLAG process and predicting the final quality of REBCO superconductive thin films. By bridging combinatorial synthesis,advanced characterization, and data-driven analysis, this study establishes a workflow that can be leveraged in the future tofacilitate the scaling-up of TLAG technology for industrial applications.