Material Extrusion Additive Manufacturing (MEAM) is a disruptive technology that builds parts layer by layer. It enables the creation of complex geometrical parts with high versatility and reduced environmental impact. As the demand for sustainable practices increases, the use of recycled thermoplastic filaments has become a key focus. This shift is driven by the low recycling rates in Additive Manufacturing (AM) and the urgent need to minimize material waste. In pursuit of this outcome, assessing the recyclability of AM waste is essential. However, current approaches are often unsustainable, costly, resource-intensive, and require technical expertise. To address these challenges, this research proposes a methodological framework for automating the recyclability assessment of Acrylonitrile Butadiene Styrene (ABS) waste generated from MEAM. To facilitate the development of this framework, a systematic literature review was conducted to identify the key recyclability assessment metrics and the parameters that have the greatest influence on them. Further, the review explored the use of Deep Learning techniques for predicting the quality of additively manufactured parts. The proposed framework aims to facilitate the creation of automated tools for evaluating ABS waste for potential reuse in AM. The paper concludes with a summary of the key findings and directions for future research.