Phosphorylation of intrinsically disordered proteins/regions (IDPs/IDRs) has a profound effect in biological functions such as cell signaling, protein folding or unfolding, and long-range allosteric effects. However, here we focus on two IDPs, namely 83-residue IDR transcription factor Ash1 and 92-residue long N-terminal region of CDK inhibitor Sic1 protein, found in Saccharomyces cerevisiae, for which experimental measurements of average conformational properties, namely, radius of gyration and structure factor, indicate negligible changes upon phosphorylation. Here, we show that a judicious dissection of conformational ensemble via combination of unsupervised machine learning and extensive molecular dynamics (MD) trajectories can highlight key differences and similarities among the phosphorylated and wild-type IDP. In particular, we develop Markov state model (MSM) using the latent-space dimensions of an autoencoder, trained using multi-microsecond long MD simulation trajectories. Examination of structural changes among the states, prior to and upon phosphorylation, captured several similarities and differences in their backbone contact maps, secondary structure, and torsion angles. Hydrogen bonding analysis revealed that phosphorylation not only increases the number of hydrogen bonds but also switches the pattern of hydrogen bonding between the backbone and side chain atoms with the phosphorylated residues. We also observe that although phosphorylation introduces salt bridges, there is a loss of the cation-π interaction. Phosphorylation also improved the probability for long-range hydrophobic contacts and also enhanced interaction with water molecules and improved the local structure of water as evident from the geometric order parameters. The observations on these machine-learnt states gave important insights, as it would otherwise be difficult to determine experimentally which is important, if we were to understand the role of phosphorylation of IDPs in their biological functions.