Currently, 40% of the world’s population experiences limited access to water, a figure projected to worsen due to climate change, thereby endangering crop yields, public health, and ecosystems at an unprecedented scale. To combat this, weather modification strategies are increasing in popularity—one example is cloud seeding, where particles called Cloud Condensation Nuclei (CCN) are released into the atmosphere, providing a “nucleus” for water vapor to condense on, thus accelerating droplet growth. Cloud seeding is promising, but there has been limited exploration of optimizing CCNs beyond salt compounds, and there is no clear indication of how effective cloud seeding is. This project overcomes these shortcomings by introducing three types of surfactants to improve the hygroscopicity of CCNs: cationic (Cetyltrimethylammonium Bromide, CTAB), amphoteric (Cocamidopropyl Betaine, CAPB), and anionic (Sodium Laureth Sulfate, SLES). The surfactant-based seeding solution is released via a nebulizer in a cloud chamber simulating atmospheric conditions of supersaturation, sub-dew point temperature, and natural aerosol particles. A measurement of the attenuation of a laser beam directed through the chamber for each solution at different concentrations of surfactants shows that CTAB, which has the highest Critical Micelle Concentration, produces the largest droplets and, therefore, by previously established relationships, higher precipitation yields—a finding that has not been experimentally explored. To quantify this success, a multi-modal classification model was trained on satellite imagery, validated with an F1-score of 93%, and then applied to experimental data, finding that surfactant-based seeding is approximately twice as effective as standard seeding.