Pharmaceutical regulatory approval delays often occur when unresolved chemistry, manufacturing, and controls deficiencies intersect with weak manufacturing readiness, yet agency correspondence detailing these issues is rarely leveraged as predictive evidence before final regulatory action. Currently, sponsors assess submission risk primarily through expert judgment, historical experience, and qualitative escalation processes, leaving an unmet need for an interpretable modeling framework capable of forecasting delays while pinpointing specific, remediable drivers of risk. This article proposes explainable machine learning models that integrate text-derived deficiency features with structured manufacturing readiness indicators to predict regulatory approval delays in a transparent, auditable, and actionable manner for regulatory affairs and compliance teams. By using natural language processing to encode deficiency topics and severity, and employing SHAP explanations to decompose predicted delay risk into individual feature contributions, a conceptual gradient-boosted classifier or survival model could, for example, identify a high-risk submission where delays are driven by aseptic process validation concerns and an unfavorable recent inspection outcome. Linking predictions directly to deficiency language and operational readiness signals, rather than providing an opaque risk score, would enable sponsors to prioritize targeted remediation, allocate resources effectively, and improve overall submission quality.