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Artificial intelligence is increasingly being positioned as a transformative tool in pharmaceutical formulation and process development because it can model complex relationships among molecular properties, excipient behaviour, formulation variables, processing conditions, and product performance. Machine learning, deep learning, hybrid modelling, and optimisation algorithms are now used to support decisions that were previously dominated by empirical screening and expert judgement. Despite this promise, the practical translation of artificial intelligence into pharmaceutical development remains uneven. Many models demonstrate high retrospective accuracy but provide limited mechanistic insight, weak interpretability, and uncertain relevance when moved beyond the specific datasets, formulations, equipment, or scales on which they were trained. This critical review examines artificial intelligence in pharmaceutical formulation and process development through three linked lenses: explainability, transferability, and regulatory trust. It argues that these issues are not secondary implementation details but core determinants of whether artificial intelligence can become credible within quality-driven pharmaceutical development. The analysis shows that artificial intelligence can support formulation and process understanding only when predictive performance is accompanied by transparent reasoning, domain-aware validation, lifecycle governance, and evidence of transferability across development contexts. A coordinated pathway involving explainable-by-design models, standardised transferability testing, and regulatory learning environments is required to move pharmaceutical artificial intelligence from technical promise toward justified regulatory trust.

Algorithmic tools are becoming embedded in pharmaceutical technology development, from formulation screening and excipient selection to process modelling, scale-up prediction, analytical method optimisation, and manufacturing control. These tools increasingly influence decisions that were historically made through human expert judgement, experimental iteration, and quality-system review. This shift creates a governance challenge because algorithmic recommendations may affect product quality, process robustness, patient safety, and regulatory compliance. Yet accountability for such decisions is often distributed ambiguously across data scientists, formulation scientists, process engineers, quality assurance units, regulatory teams, software vendors, and corporate management. The objective of this article is to construct an original governance framework for accountable algorithmic decision-making in pharmaceutical technology development. The framework is designed for settings where artificial intelligence, machine learning, and related computational decision-support tools influence formulation, process, analytical, or quality decisions. The proposed framework argues that accountability must be designed into algorithmic pharmaceutical development rather than retrofitted after model deployment. It defines algorithmic decision types, clarifies accountability triggers, and positions governance as part of the pharmaceutical quality system rather than as a separate digital compliance layer.