Despite remarkable progress in artificial intelligence diagnostics (and medicine) to enhance Precision ART, Latent Space embeddings can only be macroscopically defined by human eyes, and training labels remain noisy due to hidden genetic abnormalities that elude current diagnosis, a lack of temporal contextualization through prophet modelling over clinical evolution [5], and subjective clinical predictors. To tackle these crucial gaps, this work proposes a unified machine learning framework that propagates from qualitative to quantitative and highly objective data-driven assessments. In first part, we transfer from macroscopic assessments to the microscopic cellular level using a YOLOv8-based object detection network trained for quantitation of bacterial inhibition zones capturing early-stage growth dynamics undetectable by naked eye. Secondly, we address the “noisy label” phenomenon in embryo grading, where seemingly normal embryos do not implant due to hidden chromosomal abnormalities that evade detection with visual examination by applying regularized Positive-Unlabeled (PU) learning and ranking instead of standard supervised learning. Third, we propose a Multi-Task Deep Learning with Dynamic Programming (MTDL-DP) architecture that abides by the biological principle of monotonically non-decreasing cell division when applied to time-lapse embryo videos, thereby rectifying erratic single-frame classifications. In order to fill the gap in objective prediction of uterine receptivity, we extract quantitative multi-modal features from the EHG and TVUS, classify them using Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), respectively.