The adrenal cortex and gonads produce steroid hormones involved in salt and glucose homeostasis, blood pressure regulation, stress response and sex differentiation. These hormones are produced via a series of enzymatic steps and metabolites of steroids from each step are excreted and measurable in urine. Inborn disorders of steroidogenesis result from genetic mutations in distinct enzymes, causing a block in hormone production and lead to several forms of Congenital Adrenal Hyperplasia and differences in sex development. Each enzyme deficiency is characterised by a distinct pattern of altered excretion of individual steroid metabolites relating to the specific enzymatic block. Ratios of urine steroid metabolites can be employed as surrogates of distinct steroidogenic enzyme activities, enabling diagnosis from single spot urine samples, ideal for use with paediatric patients. widespread use in the acute setting for diagnosis of these disorders is hampered by the considerable expertise required for interpretation. Here, we developed two approaches for assisting the detection and differentiation of inborn steroidogenic disorders. First, we have designed a clinical decision tool using biochemical analysis by established steroid precursor-to-product metabolite ratios. Second, we developed a novel steroid metabolomics approach, combining mass spectrometry-based steroid profiling with intrinsically interpretable machine learning-based data analysis. The analysis methods presented can expedite and standardise interpretation of complex urinary steroid metabolome data, making this technique more accessible to clinicians, and has excellent potential for implementation in routine clinical practice.