Preimplantation genetic testing for monogenic diseases (PGT-M) can be used to select embryos that do not develop disease phenotypes or carry disease-causing genes for implantation into the mother’s uterus, to block disease transmission to the offspring, and to increase the birth rate of healthy newborns. However, the traditional PGT-M technique has some limitations, such as its time consumption, experimental procedural complexity, and the need for a complete family or reference embryo to construct the haplotype. In this study, proband-independent haplotyping based on NGS-based long-read sequencing (Phbol-seq) was used to effectively construct haplotypes. By targeting the mutation sites of single gene disease point mutations and small fragment deletion carriers, embryos carrying parental disease-causing mutations were successfully identified by linkage analysis. The efficiency of embryo resolution was then verified by classical Sanger sequencing, and it was confirmed that the construction of haplotype and SNP linkage analysis by Phbol-seq could accurately and effectively detect whether embryos carried parental pathogenic mutations. After the embryos confirmed to be nonpathogenic by Phbol-seq-based PGT-M and confirmed to have normal copy number variation by Phbol-seq-based PGT-A were transplanted into the uterus, gene detection in amniotic fluid of the implanted embryos was performed, and the results confirmed that Phbol-seq technology could accurately distinguish normal genotype embryos from genetically modified carrier embryos. Our results suggest that Phbol-seq is an effective strategy for accurately locating mutation sites and accurately distinguishing between embryos that inherit disease-causing genes and normal embryos that do not. This is critical for Phbol-seq-based PGT-M and could help more single-gene disease carriers with incomplete families, de novo mutations or suspected germline mosaicism to have healthy babies with normal phenotypes. It also helps to reduce the transmission of monogenic genetic diseases in the population.