Artificial Intelligence (AI) systems have increasingly played important decision-making roles, requiring interpretability and transparency. Explainable AI (XAI) attempts to make AI models interpretable to humans. Yet, current explainability methods tend to present rigid, single-perspective descriptions. This paper proposes a new Syādvāda-based XAI framework, drawing upon the seven-fold conditional logic of Syādvāda from Jain philosophy for better interpretability. By integrating multi-perspective reasoning, probabilistic logic, and mathematical formulations, this framework allows AI systems to provide context-aware, pluralistic explanations. The paper explores the mathematical formulation of Syādvāda logic, depicting its application to feature attribution, fairness-aware explanations, and uncertainty quantification. The framework is experimentally validated on real-world AI models, demonstrating its effectiveness in improving interpretability. The approach unifies mathematical reasoning, philosophical logic, and AI interpretability, establishing a new benchmark for XAI.