Recent advancements in the multiscale fabrication and characterization of composite multiferroic materials for magnetoelectric coupling have flourished over the past two decades, leading to numerous translational applications. Multiferroics couple, intrinsically or extrinsically, the strain, electric, and magnetic energies, holding the promise for power-efficient microscale devices. Composite multiferroic structures usually consist of a piezoelectric phase (relating strain and electrostatic) and a piezomagnetic material (interfacing strain and magnetization), resulting in a bidirectional magnetoelectric coupling. However, this exciting class of materials suffers from a twofold shortcoming. First, the current state-of-the-art fabrication process is tedious, requiring laborious steps, with low manufacturing yield. Second, the resulting performance, reported in the recent literature, still lags behind the theoretical predictions due to manufacturing defects. This research aimed to develop a new manufacturing method based on additive manufacturing (3D printing), where the processing time, effort, and cost are expected to be substantially decreased. Given the temperature dependence of the constituents (i.e., high temperature may degrade or nullify the magnetoelectric properties), the proposed 3D printing is based on Stereolithography (SL), i.e., light-activated polymerization. SLA printing works by exposing a photopolymer to ultraviolet light, where the polymer cures upon exposure additively, resulting in the desired geometry. Moreover, the vat polymerization process is conducive to the suspension of magnetic particles within a modified photopolymer with enhanced electrical properties, resulting in an increase its piezoelectric properties. After successful demonstration of the new additive manufacturing approach, a comprehensive material characterization approach was undertaken to understand the process-structure-performance interrelationship. First, the mechanical properties of the newly manufactured composite material were investigated. The mechanical data was synthesized using machine learning approach with a newly developed error feedback algorithm. Second, the electrical properties were measured on an LCR meter to note the effect of poling on the samples. Finally, the magnetic properties were measured using a vibrating-sample magnetometer (VSM). In all, the outcomes of this research paved the way to transform the translational potential of composite multiferroic materials.