Analysis of electronic health records from Stony Brook University Hospital revealed that GLP-1 receptor agonists demonstrated superior effectiveness compared to SGLT2 inhibitors in heart failure patients, showing reduced risk for the composite outcome of all-cause mortality or heart failure-related hospitalization over one year. The study employed causal machine learning methods to estimate both population-wide and individualized treatment effects. This finding challenges current clinical practice where both drug classes are considered roughly equivalent options for heart failure management. The research represents an important step toward precision medicine in cardiovascular care, potentially helping clinicians select optimal therapies for individual patients. However, the analysis identified limited evidence for meaningful treatment individualization, with only loop diuretic use, BMI, and kidney function emerging as potential effect modifiers. Since this is a preprint awaiting peer review, these results require validation through rigorous peer assessment and replication in larger, more diverse patient populations. The single-center design and observational nature also limit generalizability, making this more of a promising proof-of-concept than definitive clinical guidance.