Artificial Intelligence, Microrobots/Nanorobots and Pharmaceutically Optimized Nanocarrier Systems for Precision Tumor Multitargeting

Precision oncology increasingly requires multi-target strategies addressing tumor heterogeneity, microenvironmental barriers, immune evasion, and drug resistance. This narrative review critically examines the convergence of artificial intelligence (AI), microrobotic/nanorobotic systems, and pharmaceutically optimized nanocarriers for precision tumor multi-targeting. It highlights emerging intelligent nanomedicine strategies while distinguishing clinical evidence for selected nanocarrier products from preclinical and conceptual integration. This review explores AI-guided formulation design and patient stratification; microrobots and nano/micromotors for active delivery and intratumoral penetration; and optimized, stimuli-responsive nanocarriers for co-delivery of chemotherapeutics, immunomodulators, nucleic acids, and gene-editing agents. Tumor multi-targeting, microenvironment-responsive release, and synchronized drug delivery are critically assessed. AI-guided formulation studies include computational prediction and prospective laboratory validation, whereas active delivery platforms have predominantly been evaluated in vitro and in animal models. Preclinical and conceptual findings suggest that integrating AI-guided design, active navigation, and responsive nanocarriers may enhance spatial and temporal delivery control; reductions in systemic toxicity require system-specific validation. Study-specific quantitative matrices compare model validation, actuation conditions, payloads, release characteristics, and exposure measures, explicitly identifying missing data and non-comparable endpoints. Target prioritization incorporates biological validity, druggability, delivery feasibility, and clinical actionability. Investigational digital twins require longitudinal data quality control, calibrated uncertainty, drift surveillance, and clinician oversight. Although selected robotic–carrier combinations demonstrate preclinical benefits, fully integrated platforms enabling patient-specific adaptive treatment lack clinical validation. Translation requires indication-specific comparisons with simpler formulations, reproducible manufacturing, validated navigation, and prospective benefit–risk assessment. Future research should prioritize pharmaco-nanotechnologies, refined cancer molecular classification, and clinician-governed decision support before adaptive AI-assisted multi-target nanomedicine.