Drug-induced liver injury (DILI) represents a critical safety concern for drug development, regulatory oversight, and clinical practice, with substantial economic and public health implications. While predicting DILI risk in humans has garnered significant attention, the associated chemical space has remained insufficiently explored. This study addresses this gap through a comprehensive computational approach, leveraging machine learning (ML) to investigate structural determinants of DILI risk systematically. The study focuses on three key objectives: (i) exploring the chemical space and scaffold diversity associated with DILI; (ii) employing fragment-based approaches to identify structural alerts (SAs) that influence DILI risk; and (iii) developing supervised ML models to not only predict DILI risk but also elucidate the structural significance of molecular fingerprints. To broaden accessibility, we introduce pDILI_v1, a Python-based web application available at https://pdiliv1web.streamlit.app/. This user-friendly platform facilitates the prediction and visualization of DILI risk, enabling both experts and nonexperts to screen compounds effectively. Additional formats, including a Google Colab notebook and a graphical user interface (GUI) for Windows, ensure flexibility for diverse user needs. The proposed models demonstrate the potential for early identification of hepatotoxic risks in drug candidates, providing critical insights into drug discovery and development. By integrating ML-driven predictions with chemical space analysis, this research advances the field of drug safety evaluation, contributing to the development of safer pharmaceuticals and mitigating the risks of DILI.