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Peri-implantitis is a common and biologically complex complication of dental implants, often diagnosed only after irreversible bone loss has occurred. This study aimed to develop a personalized risk assessment tool by integrating clinical, molecular, and microbial data. A total of 124 participants were stratified into peri-implantitis and healthy/mucositis groups. Clinical parameters, including clinical attachment loss (CAL), were measured, and biological samples were collected for active-matrix metalloproteinase-8 (aMMP-8) quantification, quantitative microbial profiling, and MMP-8 polymorphism genotyping. Multivariable logistic regression identified CAL, elevated aMMP-8 (>20 ng/mL), and Staphylococcus epidermidis load as independent predictors of peri-implantitis, yielding a highly accurate model (Area Under the Curve = 0.982, accuracy = 94.2%). Additionally, MMP-8 mRNA gene expression and aMMP-8 levels were significant predictors of bleeding on probing. Based on the regression model, a clinically applicable, point-based risk assessment tool was developed that integrates three key predictors—CAL, aMMP-8, and MMP-8 SNP (T allele, rs11532004). Weighted scoring enabled stratification into low-, moderate-, and high-risk categories, with scores ranging from 0 to 7. This system demonstrated strong alignment with clinical diagnosis and offers a personalized, screening approach to support early intervention in peri-implantitis. The findings highlight the diagnostic value of combining host-response biomarkers and microbial profiling in assessing peri-implant disease risk. This model advances precision dentistry by enabling early detection and personalized monitoring of peri-implantitis, supporting more targeted prevention and intervention strategies.