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Background and Objective: Unexplained infertility is a major challenge in reproductive medicine and requires advanced diagnostic approaches to identify the underlying factors accurately. This study aims to evaluate the utility of risk factor analysis and a gene panel in diagnosing unexplained infertility using the next-generation sequencing (NGS) technology. Our study aimed to characterize and identify risk and genetic factors associated with unexplained infertility. Materials and methods: A cohort of patients with unexplained infertility was comprehensively screened for risk factors and genetic variations using a targeted gene panel (10 couples with unexplained infertility (UI) and 36 fertile couples). 108 articles were selected (58 on female infertility and 50 on male infertility) presenting genes that may be associated with unexplained infertility. A gene panel for unexplained infertility was compiled based on the literature data. A customized virtual panel was created from the exome sequencing data. Results: In the female group, controls had a higher mean age, while in the male patients, both groups were similar in terms of age. Both gender groups had comparable BMI values. No significant associations ( p > 0.05) between risk factors and unexplained infertility were found when evaluating anthropometric parameters and other sociodemographic characteristics. In two male patients (20%), a molecular defect was detected in NGS variants classified aspossible benign and probably benign In particular, missense variants were identified in the UGT2B7 and CATSPER2 genes, A molecular defect classified as probably damaging was found in five female patients (50%). In particular, missense variants were identified in the CAPN10 , MLH3 , HABP2 , IRS1 , GDF9 , and SLC19A1 genes. Conclusions: The study emphasizes that unexplained infertility is often related to mechanisms beyond causative mutations and highlights the need for integrative genomic research involving broader gene panels and multi-faceted approaches, including transcriptomics and epigenetics, to uncover latent genetic predispositions.