Based on expert chemists' analysis, we propose an evaluation criterion that combines both the score and route length metrics, allowing us to classify route scores from the model into three distinct categories. When compared with the chemists' ratings, our model demonstrates strong predictive accuracy, aligning with expert assessments 74 % of the time. Although the model encounters some difficulty distinguishing between adjacent categories, it effectively avoids extreme misclassifications. This highlights the potential for further refinement through expert-augmented model tuning, either by fine-tuning category boundaries or adopting a human-in-the-loop (HITL) framework. By embedding expert knowledge, the model captures the implicit evaluation standards prevalent in the chemistry community, accounting for both reaction-level intricacies and complex route-level preferences. It is important to note that our route scoring model when combined with AIZynthFinder as a route generator significantly outperforms the state-of-the-art DirectMultiStep retrosynthesis tool with the top-1 ranking accuracy of 38 %. When compared with the state-of-the-art SOA framework, 11 our system is better in suggesting the best or the most strategic route (79% vs 84% accuracy in favor of our framework). Importantly, it offers first-in-class multi-step route quality and feasibility assessment based on a combination of machine learning and human expertise with a 3-bin classification accuracy of 76%.