Biological circuits in synthetic biology are designed to perform logical functions, similar to their electrical counterparts, inside living cells. However, unlike electronic parts, biological parts are often noisy and error-prone, requiring many iterations of trial and error, termed the design-build-test (DBT) cycle, before a successful design is achieved. These cycles are a major bottleneck for progress in the field. The emergence of high-throughput experimental techniques combined with machine learning (ML) algorithms, provide the ingredients for a potential “big-data” solution that can generate predictive capability to overcome the DBT bottleneck. In this work, we apply such an approach to the design of RNA cassettes used in gene editing and RNA tracking systems. RNA cassettes are typically made of repetitive hairpin-structured binding sites, therefore hindering their retention, synthesis, and functionality. Here, we carried out an oligo-library-based experiment to generate thousands of new binding sites for the phage coat proteins (CP) of bacteriophages MS2, PP7, and Qβ. We then applied a neural network to study and predict protein binding. We then designed new predicted non-repetitive cassettes and validated their functionality in mammalian cells. Taken together, we developed a tool for improving imaging experiments, with more robust measurements and quantitative data.