Understanding the spatial and temporal dynamics of gene expression is crucial for unraveling molecular mechanisms underlying various biological processes. While traditional methods have offered insights into gene expression patterns, they primarily focus on mature mRNA transcripts, lacking real-time visualization of newly synthesized or nascent transcription events. Recent advancements in monitoring nascent transcription in live cells provide valuable insights into transcriptional dynamics. However, such approaches are limited in mammalian embryos. Addressing this gap, we optimized a single molecule fluorescent in situ hybridization (smFISH) technique and coupled it with deep learning algorithms to automate detection of nascent transcription in mouse embryonic tissue samples. Our method enables precise quantification and comparison of nascent transcripts within tissue sections, offering reproducible results and potential applications in studying gene expression dynamics across various developmental stages.