AI-Driven Universal DNA Extraction and Quality Prediction Framework for Cross-Kingdom Genomic Applications

High-quality genomic DNA is fundamental for molecular diagnostics, precision medicine, agricultural biotechnology, microbial genomics, and biodiversity conservation. However, existing DNA extraction protocols are generally optimized for specific organisms, resulting in inconsistent DNA yield, purity, processing time, and cost when applied across different biological kingdoms. Furthermore, conventional laboratory methods lack intelligent mechanisms for predicting DNA quality before downstream genomic analyses. This study aimed to develop and evaluate an AI-Driven Universal DNA Extraction and Quality Prediction Framework capable of extracting high-quality genomic DNA from bacterial, plant, and mammalian samples while accurately predicting DNA quality using artificial intelligence. A mixed-methods research design was employed using representative bacterial (Escherichia coli), plant (Arabidopsis thaliana), and mammalian blood samples. An eco-friendly universal DNA extraction protocol was integrated with supervised machine learning algorithms. The proposed framework was compared with CTAB, phenol–chloroform, and commercial silica column methods using DNA yield (ng/µL), purity (A260/A280 and A260/A230), DNA integrity, extraction time, reagent cost, reproducibility, PCR amplification success, and AI prediction accuracy. The proposed framework achieved an average DNA purity of 1.87–1.95 (A260/A280), increased DNA yield by 22.8%, reduced extraction time by 34.5%, lowered reagent cost by 41.2%, and achieved a 97.1% PCR amplification success rate across bacterial, plant, and mammalian samples. The AI-based quality prediction model attained 96.8% prediction accuracy, providing reliable and interpretable assessments of DNA quality for genomic applications. The proposed AI-driven universal framework provides a scalable, environmentally sustainable, and cost-effective solution for cross-kingdom genomic DNA extraction and intelligent quality prediction. Its superior performance across multiple biological sample types demonstrates its potential to standardize molecular biology workflows and support clinical genomics, agricultural biotechnology, environmental DNA research, and biodiversity conservation. Keywords: Machine Learning.; Keywords: Artificial Intelligence; Universal DNA Extraction; DNA Quality Prediction; Cross-Kingdom Genomics