Despite diffusion tensor imaging (DTI) evidence for widespread fractional anisotropy (FA) reductions in the brain white matter of patients with bipolar disorder, questions remain regarding the specificity and sensitivity of FA abnormalities as opposed to other diffusion metrics in the disorder. We conducted a whole‐brain voxel‐based multicompartment diffusion MRI study on 316 participants (i.e., 158 patients and 158 matched healthy controls) employing four diffusion metrics: the mean diffusivity (MD) and FA estimated from DTI, and the intra‐axonal signal fraction (IASF) and microscopic axonal parallel diffusivity (Dpar) derived from the spherical mean technique. Our findings provide novel evidence about widespread abnormalities in other diffusion metrics in BD. An extensive overlap between the FA and IASF results suggests that the lower FA in patients may be caused by a reduced intra‐axonal volume fraction or a higher macromolecular content in the intra‐axonal water. We also found a diffuse alteration in MD involving white and grey matter tissue and more localised changes in Dpar. A Machine Learning analysis revealed that FA, followed by IASF, were the most helpful metric for the automatic diagnosis of BD patients, reaching an accuracy of 72%. Number of mood episodes, age of onset/duration of illness, psychotic symptoms, and current treatment with lithium, antipsychotics, antidepressants, and antiepileptics were all significantly associated with microstructure abnormalities. Lithium treatment was associated with less microstructure abnormality. A whole‐brain voxel‐based approach combining DTI and SMT diffusion‐derived metrics to reveal specific brain tissue microstructure abnormalities in bipolar disorder (BD) was carried out. We also evaluate the sensitivity of various diffusion metrics for the automatic diagnosis of BD through machine learning algorithms and cross‐validation. Finally, we look for differences between BD subtypes and the possible effect of duration of illness, age at onset, history of psychosis, and pharmacological treatment with lithium, antidepressant, antipsychotic, and antiepileptic drugs.