3D bioprinting emerges as a prominent tool for regenerative medicine and biomedical applications. Among extrusion-based, laser-assisted, and drop-on-demand techniques, extrusion-based bioprinting receives greater attention due to its capability to handle a variety of material types while supporting higher cell densities. Three major tasks in asuccessful bioprinting process are bioink formulation, optimizing print process parameters (e.g., extrusion pressure, nozzle diameter, print speed and distance, and environment) to obtain thedesired construct, and finally targeted cell survivability and growth in the construct. Mostexisting works focus on each task category and consider each process optimization individually. However, the goal is to make bioprinting successful with thesequential success of all these three task categories. In this work, we have considered all three task categories (e.g., bioink formulation, optimizing printing parameters, and fostering targeted cell growth and survivability) to understand the success of thebioprinting process. A classification-based machine learning has been adopted to infer the ultimate extrusion-based printing success. A total of 72 experimental bioprinted datasets including various printing process parameters, rheological properties, filament fidelity, and cell viability have been used in this work. Our proposed model predicted bioprinting success with above 80% accuracy. These results would help to understand and predict the possible outcomes of a printing process, possibly saving resources and time for the researcher and practitioners.