This study investigates the genetic landscape and prognostic significance of mutations in 944 patients with myelodysplastic syndromes (MDS). Using high-throughput DNA sequencing, the researchers identified mutations in 845 out of 944 patients (89.5%), with a median of 3 mutations per patient. Key mutations included *TET2*, *SF3B1*, *ASXL1*, *SRSF2*, *DNMT3A*, and *RUNX1*, each present in over 10% of cases. These mutations were associated with higher-risk groups and blast elevation. Survival analysis in 875 patients revealed that 25 out of 48 genes affected overall survival. A novel prognostic model combining 14 genes with conventional factors (Model-1) and a gene-only model (Model-2) were developed, both successfully classifying patients into four risk groups with significantly different 3-year survival rates. The models outperformed the International Prognostic Scoring System (IPSS) and its revised version (IPSS-R). The study highlights the importance of comprehensive genetic profiling in MDS for improved diagnosis, risk stratification, and prognosis.This study investigates the genetic landscape and prognostic significance of mutations in 944 patients with myelodysplastic syndromes (MDS). Using high-throughput DNA sequencing, the researchers identified mutations in 845 out of 944 patients (89.5%), with a median of 3 mutations per patient. Key mutations included *TET2*, *SF3B1*, *ASXL1*, *SRSF2*, *DNMT3A*, and *RUNX1*, each present in over 10% of cases. These mutations were associated with higher-risk groups and blast elevation. Survival analysis in 875 patients revealed that 25 out of 48 genes affected overall survival. A novel prognostic model combining 14 genes with conventional factors (Model-1) and a gene-only model (Model-2) were developed, both successfully classifying patients into four risk groups with significantly different 3-year survival rates. The models outperformed the International Prognostic Scoring System (IPSS) and its revised version (IPSS-R). The study highlights the importance of comprehensive genetic profiling in MDS for improved diagnosis, risk stratification, and prognosis.