ontemporary customers interact with brands through dense, heterogeneous streams of data generated across websites, mobile applications, social media, conversational interfaces, and physical touchpoints. These interactions form complex, nonlinear customer journeys that can no longer be adequately understood through channel-specific or unimodal analytics. This paper proposes a comprehensive framework for multimodal big data analytics aimed at optimizing customer journeys across digital platforms. The framework integrates structured transactional logs, clickstream sequences, textual reviews, visual content, and interactional signals (voice and chat) into a unified representation using deep learning–based encoders and cross-modal fusion mechanisms. On top of this representation, sequence modeling and graph-based learning are used to infer journey paths, predict next-best actions, estimate churn and conversion probabilities, and support real-time decisioning for personalized interventions. Architecturally, the framework leverages scalable data lakehouse infrastructures and streaming pipelines to support continuous ingestion, identity resolution, and online model updating under strict latency and governance constraints. Conceptually, the work advances customer journey analytics by moving from static, stage-based representations to dynamic, multimodal trajectories that can be optimized at both individual and segment levels. The paper also articulates a research agenda on explainable multimodal models, privacy-preserving learning, and evaluation protocols that jointly consider customer experience, operational efficiency, and ethical concerns. Empirically, the proposed approach is positioned to deliver measurable improvements in conversion, retention, and cross-channel consistency by enabling organizations to orchestrate journeys based on a holistic understanding of customer behavior embedded in multimodal big data.