Body condition scoring (BCS) is a non-invasive way to measure the amount of fat present on the cow’s body. Cows with low or high BCS are at a higher risk of developing metabolic disease and decreased reproductive performance. The objective of this study was to evaluate the effect of automated collected BCS around calving on subclinical and clinical disease outcomes. This study was conducted from September 2016 to September 2017 on a 3,200-cow commercial dairy in Indiana. Subclinical ketosis and milk fever status were determined for each cow from one blood sample taken ≤7 DIM. Blood was analyzed for BHB and calcium concentrations. Positive subclinical cases of ketosis and milk fever were classified at ≥1.2 mmol/L BHB and <8.6 mmol/L calcium, respectively. Clinical disease data, for metritis, milk fever, and ketosis, were collected by farm personnel and recorded electronically. Daily BCS was recorded automatically, on a 1 to 5 scale, from an automated BCS camera (DeLaval International AB, Tumba, Sweden) throughout lactation and retrieved electronically. The technology has shown to be equivalent to manual scoring (r = 0.77, P < 0.001). Using Pearson correlation procedure, BCS at calving, dry-off, and test day had no correlation with subclinical concentrations of BHB and calcium. Odds of subclinical and clinical disease cases were determined by binary logistic regression. Cows with a BCS ≥3.75 at dry-off had increased odds of having a positive subclinical ketosis case (OR = 3.62; CI: 1.33, 9.86; P < 0.01). For every 0.3 BCS gain from calving to 7 DIM, cows were 35% less likely to develop metritis ≤14 DIM (OR = 0.65; CI: 0.53, 0.80; P < 0.01). Displaced abomasum ≤150 DIM was less likely in cows at heavier condition, as BCS increased by 0.2, at 21 DIM (P < 0.01). Assessment of BCS using an automated system may be a useful measure to alert for the risks of metabolic diseases around calving.