International Journal of Scientific Research and Engineering Development

Artificial Intelligence (AI) has been implemented to predict judicial decision-making amid issues such as delay, inconsistency, and bias in judicial systems. However, many of the models of AI are not transparent or fair. This paper proposed a Predictive Justice framework that utilizes Explainable AI techniques LIME, SHAP to provide interpretable and transparent decision support. It also utilizes Multimodal Learning which leverages textual, contextual, numerical, audio, and video modalities to incorporate case evidence into consideration of case outcomes while improving prediction capabilities.The validity of the Experimental for Predictive Justice Framework showed improvements in accuracy, accountability, and ethical trustworthiness. Constructed to serve as a trusted, transparent decision-support tool for practitioners, the framework couples AI-driven adjudicated with the norms, principles, and morality of society.