Analysis of 41,076 UK Biobank participants found that ECG measurements taken during rest, exercise, and recovery independently predicted major adverse cardiac and cerebrovascular events over 12.5 years of follow-up. Two ECG scoring systems - conventional parameters (C-ECG) and neural network analysis (ECGAI) - showed hazard ratios of 1.76 and 1.18 respectively per standard deviation increase. However, when added to established QRISK3 clinical factors, both ECG approaches improved prediction accuracy by only 0.03 C-index points. This represents a modest but measurable enhancement in cardiovascular risk stratification. The findings align with growing evidence that advanced cardiac monitoring provides incremental predictive value beyond traditional risk factors like age, blood pressure, and cholesterol. The technology shows particular promise for women, where ECGAI improved risk classification significantly. Practical implications remain limited given the small improvement margins, though ECG-based screening could potentially identify high-risk individuals missed by conventional assessments. As this is a preprint awaiting peer review, the methodology and statistical approaches require validation before clinical implementation. The research represents an incremental advance in precision cardiology rather than a paradigm shift.