<![CDATA[Islamic jurisprudence traditionally relies on textual interpretation, analogical reasoning (qiyās), and scholarly consensus to derive legal judgments. However, in contemporary legal systems, particularly in domains such as forensic science, financial litigation, and family law, the need for empirical and objective evidentiary standards is increasing. This necessitates a reconsideration of classical epistemological tools in Islamic law. This article aims to explore how statistical reasoning and probabilistic inference can serve to modernize and complement traditional Islamic evidentiary principles. It aims to identify whether these tools can offer a more precise, replicable, and just framework without compromising the ethical integrity of Shariʿah. A doctrinal and comparative analysis was conducted, incorporating classical legal maxims and statistical inference models. Empirical case studies from Islamic courts and hybrid legal systems were evaluated alongside predictive models such as Bayesian probability, error rate thresholds, and likelihood ratios. The methodology also utilized textual hermeneutics to explore maqāṣid al-Sharīʿah compliance. Integration of statistical inference mechanisms—particularly in the domain of hudūd, tazīr, and personal status cases—indicates a measurable enhancement in judicial consistency and reduction in evidentiary ambiguity. Courts that applied forensic and data-driven models exhibited lower reversal rates and increased public confidence, while remaining compliant with foundational Sharīʿah values when guided by juristic oversight. Incorporating statistical methodologies into Islamic legal procedures does not replace traditional methods but rather reinforces them with quantifiable validity. This evolution can provide a coherent framework for contemporary challenges while remaining aligned with the core objectives of justice, fairness, and social welfare as enshrined in Islamic jurisprudence.]]>
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