PURPOSE: To investigate the association between retinal microstructure and cone and rod function in geographic atrophy (GA) secondary to age-related macular degeneration (AMD) by using artificial intelligence (AI) algorithms. DESIGN: Prospective, observational case series. METHODS: A total of 41 eyes of 41 patients (75.8 $±$ 8.4 years old; 22 females) from a tertiary referral hospital were included. Mesopic, dark-adapted (DA) cyan and red sensitivities were assessed by using fundus-controlled perimetry (microperimetry''); and retinal microstructure was assessed by using spectral-domain optical-coherence-tomography (SD-OCT), fundus autofluorescence (FAF), and near-infrared-reflectance (IR) imaging. Layer thicknesses and intensities and FAF and IR intensities were extracted for each test point. The cross-validated mean absolute error (MAE) was evaluated for random forest-based predictions of retinal sensitivity with and without patient-specific training data and percentage of increased mean-squared error (%IncMSE) as measurement of feature importance. RESULTS: Retinal sensitivity was predicted with a MAE of 4.64 dB for mesopic, 4.89 dB for DA cyan, and 4.40 dB for DA red testing in the absence of patient-specific data. Partial addition of patient-specific sensitivity data to the training sets decreased the MAE to 2.89 dB, 2.86 dB, and 2.77 dB. For all 3 types of testing, the outer nuclear layer thickness constituted the most important predictive feature (35.0, 42.22, and 53.74 %IncMSE). Spatially resolved mapping of inferred sensitivity'' revealed regions with differential degrees of mesopic and DA cyan sensitivity loss outside of the GA lesions. CONCLUSIONS: Inferred sensitivity'' accurately reflected retinal function in patients with GA. Mapping of inferred sensitivity'' could facilitate monitoring of disease progression and serve as ``quasi functional'' surrogate outcome in clinical trials, especially in consideration of retinal regions beyond areas of GA.