Gene Regulatory Networks (GRNs) represent interconnected groups of genes that collaboratively regulate cellular functions. This intricate network consists of transcription factors (TFs), proteins, and non-coding RNAs that control the activation or suppression of target genes, playing a vital role in shaping cellular behavior, development, and responses to environmental signals. In hematopoietic stem and progenitor cells (HSPCs), GRNs are essential for mapping the pathways that guide the differentiation of these multipotent stem cells into diverse blood cell lineages. By analyzing GRNs, researchers can identify the transcription factors responsible for directing HSPCs toward erythrocytes, leukocytes, or platelets. This knowledge deepens our understanding of normal hematopoiesis and the regulatory disruptions associated with hematological diseases. This study aimed to construct GRNs for HSPCs using single-cell RNA sequencing (scRNA-seq) data from publicly available dataset, focusing on evaluating the consensus across multiple GRN inference tools. By leveraging the Benchmarking gEnE reguLatory network Inference from siNgle-cEll transcriptomic data (BEELINE) framework, the goal was to identify biologically relevant and robust regulatory interactions that consistently emerge across different methods. This consensus-driven approach enhances the accuracy and reliability of inferred networks by reducing the variability associated with individual algorithms.