Spintronic (spin-based electronics) technology has recently emerged by incorporating the spin degree of freedom into conventional charge-based electronics, as spintronic devices offer low power consumption and limited current leakage. Spintronics is considered for a vast amount of applications like energy harvesting, spin photovoltaics, and data storage . In magnetic materials, the spin-polarized current is readily available due to different populations of spin-up and spin-down electrons . Therefore, magnetic materials are highly favorable for spintronic devices since their spin orientation can be efficiently manipulated using an external magnetic field. Spintronic devices like magnetic field sensors and hard-disk read-heads use giant-magneto resistance (GMR) to control the electron conductivity by aligning the spin direction of two ferromagnetic materials parallel or antiparallel to each other. GMR technology is widely used in the automotive industry, mobile phones, and the medical field . Lately, antiferromagnetic materials have become attractive candidates for magnetic memories due to their benefits over ferromagnetic compounds, like their good stability under external magnetic fields, their capability of generating large magnetotransport effects, and the absence of stray fields . Therefore, both ferromagnetic and antiferromagnetic materials are vital for developing next-generation spintronic applications. Designing computational methods for discovering novel ferromagnetic and antiferromagnetic materials is beneficial for the evolution of spintronic technology. Density functional theory (DFT) was widely utilized to study the potential magnetic materials . It is required to compare the DFT energies of nonmagnetic (NM), ferromagnetic (FM), and antiferromagnetic (AFM) states of a structure to locate the true magnetic ground state. One of the challenges of using DFT for the above purpose is the availability of multiple AFM configurations for a single compound. Thus, analyzing a large number of materials using DFT to find suitable candidates for spintronic applications is challenging. To reduce the computational burden, we are interested in informatics-guided approaches along with DFT for designing novel spintronic materials. Xia et al. predicted Fe 3 CoB 2 magnetic compound employing machine learning (ML)-guided adaptive feedback method with DFT, and also they synthesized it using a conventional arc-melting process . ML models were also developed by Long et al. for intermetallic compounds to classify the AFM and FM materials and predict their Curie temperatures. Lu et al. developed an adaptive ML framework to search the chemical space with over 2 × 10 5 candidates to realize new 2-dimensional magnetic compositions . However, it should be noted that most of those available machine learning techniques predict only the magnetic properties and compositions. They are not capable of predicting the compositions along with their structures.