This thesis presents an uncertainty-aware, photorealistic SLAM framework based on 3D Gaussian Splatting. Conventional SLAM and Gaussian-based mapping systems often face instability under dynamic lighting, sensor noise, and scene variations, as they do not account for the propagation of uncertainty across geometric, photometric, and temporal domains. To address this limitation, this work incorporates uncertainty modeling in Gaussian-splat representations and develops a confidence-aware SLAM pipeline. We first introduce a dual-depth fusion mechanism that integrates RGB-D sensor measurements with Gaussian-rendered depth, each weighted by per-pixel variance derived from a calibrated noise model. Second, a temporal uncertainty graph is formulated to monitor merge and split events among Gaussians, serving as an indicator of local scene consistency.