Phishing attacks and malicious QR codes constitute two of the most prevalent vectors of cybercrime, accounting for billions of dollars in financial losses annually. Existing defences rely on server-dependent machine learning pipelines or easily bypassed keyword heuristics that produce unacceptable false-positive rates on legitimate sites. This paper presents AIPhiShield, a browser-native cybersecurity tool that replaces heuristic match-ing with a Logistic Regression classifier trained on 20 structural URL features using Python and scikit-learn, then exported as a 2.5 KB JSON weight file and executed entirely within the browser via a custom JavaScript inference engine. No URL is transmitted to any external server for machine learning scor-ing, preserving user privacy. Detection is augmented by cross-referencing the OpenPhish live phishing feed and a curated 52-entry compound-phrase blacklist. The integrated system ad-ditionally provides QR image scanning, live webcam QR scan-ning, an LLM-powered cybersecurity chatbot routed through a Flask proxy that conceals the API key from frontend code, voice input, and geolocation-enriched scan history. The trained model achieves 100% accuracy, precision, recall, and F1-score on a stratified 72-sample test set, with zero false positives and zero false negatives. Feature importance analysis identifies HTTPS usage, high-risk top-level domain, and raw IP address as the three strongest predictors.