The article introduces the **bnlearn** R package, which provides a comprehensive set of tools for learning the structure of Bayesian networks from both discrete and continuous data. The package includes constraint-based and score-based algorithms, conditional independence tests, and network scores. It supports parallel computing and offers advanced plotting options through the **Rgraphviz** package. The author discusses the theoretical foundations of Bayesian networks, including their graphical representation and the Markov property. The package's implementation details are outlined, covering structure learning algorithms, conditional independence tests, and network scores. Practical examples are provided to demonstrate the package's capabilities, such as the ALARM network and the examination marks dataset. The article also compares **bnlearn** with other R packages for Bayesian network analysis and concludes by highlighting the versatility and performance of **bnlearn** in handling experimental data analysis.The article introduces the **bnlearn** R package, which provides a comprehensive set of tools for learning the structure of Bayesian networks from both discrete and continuous data. The package includes constraint-based and score-based algorithms, conditional independence tests, and network scores. It supports parallel computing and offers advanced plotting options through the **Rgraphviz** package. The author discusses the theoretical foundations of Bayesian networks, including their graphical representation and the Markov property. The package's implementation details are outlined, covering structure learning algorithms, conditional independence tests, and network scores. Practical examples are provided to demonstrate the package's capabilities, such as the ALARM network and the examination marks dataset. The article also compares **bnlearn** with other R packages for Bayesian network analysis and concludes by highlighting the versatility and performance of **bnlearn** in handling experimental data analysis.