IntelliGenes, another ML application in biomarker identification, is a revolutionary ML pipeline for multi-genomics biomarker research with excellent accuracy for illness prediction ( ). Using multi-genomic, clinical, and demographic data, this unique approach combines statistical methods with ML algorithms ( ). At the heart of Intelli-Genes is the Intelligent Gene (I-Gene) score, a new metric that assesses biomarkers' ability to predict complex features. IntelliGenes uses Recursive Feature Elimination, Random Forest, Support Vector Machine, XGBoost, k-Nearest Neighbours, Multi-Layer Perceptron, and soft and hard voting classifiers with Pearson correlation, Chi-square test, and ANOVA. IntelliGenes can find disease-associated biomarkers that traditional approaches miss with this combo. The method starts with AI/ML-ready Clinically Integrated Ge-nomics and Transcriptomics (CIGT) data, including patient age, gender, race, ethnicity, diagnoses, and RNA-seq gene expression data. IntelliGenes ranks the top percentage of multi-genomic data-based profiles to predict diagnoses accurately. Also, IntelliGenes' personalised early diagnosis of common and unusual diseases is a significant benefit. Biomarkers' relevance in disease prediction is categorised by the novel I-Gene score. The I-Gene score measures biomarkers' weighted utility and biological system expression using SHAP and HHI. This directionality lets researchers establish whether biomarker overexpression or underexpression causes disease, revealing disease processes. Intel-liGenes' versatility and adaptability allow it to be used on personal devices and highperformance computing platforms ( ). This makes it an effective tool for research and clinical applications, enabling personalised interventions and novel therapy targets ( ).